Extend reference model API with eager operator execution entrypoints

- Adds a script to generate operators.h and operators.cc
- Adds jinja2 templates for generating operators.h and operators.cc
- Adds unit tests for a subset of the operators generated
- Includes the TOSA specification as a submodule
- Adds supporting C++ and header files

Signed-off-by: Grant Watson <grant.watson@arm.com>
Change-Id: I5b60db4c56113110d8e75fe1152525d258233f9c

17 files changed, 3881 insertions, 153 deletions

diff --git a/.gitmodules b/.gitmodules
index 87ce1ef..5ed5edb 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -10,3 +10,6 @@
 [submodule "thirdparty/doctest"]
 	path = thirdparty/doctest
 	url = https://github.com/doctest/doctest.git
+[submodule "thirdparty/specification"]
+	path = thirdparty/specification
+	url = https://review.mlplatform.org/tosa/specification
diff --git a/reference_model/CMakeLists.txt b/reference_model/CMakeLists.txt
index 04b0db5..6494225 100644
--- a/reference_model/CMakeLists.txt
+++ b/reference_model/CMakeLists.txt
@@ -66,6 +66,7 @@ set(CXX_SOURCE
     src/graph_node.cc
     src/subgraph_traverser.cc
     src/func_debug.cc
+    src/operators.cc
     src/ops/op_factory.cc
     src/ops/tensor_ops.cc
     src/ops/activation_funcs.cc
diff --git a/reference_model/include/model_runner.h b/reference_model/include/model_runner.h
index 4335794..86d0056 100644
--- a/reference_model/include/model_runner.h
+++ b/reference_model/include/model_runner.h
@@ -71,6 +71,13 @@ public:
     int setInput(std::string input_name, std::vector<T>& vals);
 
     /*
+     * Set the input tensors for the model through a raw byte buffer.
+     * The input_name much match the input tensor name in the model.
+     * NOTE: setInput() must be called for each input tensor before run() is called.
+     */
+    int setInput(std::string input_name, uint8_t* raw_ptr, size_t size);
+
+    /*
      * Retrieve the output tensors from the graph after running.
      * The output_name much match the output tensor name in the model.
      * NOTE: run() must be called before outputs are retrieved.
@@ -78,6 +85,13 @@ public:
     template <typename T>
     std::vector<T> getOutput(std::string output_name);
 
+    /*
+     * Retrieve the output tensors from the graph after running in a raw byte buffer.
+     * The output_name much match the output tensor name in the model.
+     * NOTE: run() must be called before outputs are retrieved.
+     */
+    int getOutput(std::string output_name, uint8_t* raw_ptr, size_t size);
+
 private:
     std::unique_ptr<ModelRunnerImpl> model_runner_impl;
 };
diff --git a/reference_model/include/operators.h b/reference_model/include/operators.h
new file mode 100644
index 0000000..6e21e95
--- /dev/null
+++ b/reference_model/include/operators.h
@@ -0,0 +1,337 @@
+
+// Copyright (c) 2022, ARM Limited.
+//
+//    Licensed under the Apache License, Version 2.0 (the "License");
+//    you may not use this file except in compliance with the License.
+//    You may obtain a copy of the License at
+//
+//         http://www.apache.org/licenses/LICENSE-2.0
+//
+//    Unless required by applicable law or agreed to in writing, software
+//    distributed under the License is distributed on an "AS IS" BASIS,
+//    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//    See the License for the specific language governing permissions and
+//    limitations under the License.
+
+// THIS FILE IS GENERATED. DO NOT EDIT!
+// See scripts/operator_api/generate_api.py
+
+#ifndef OPERATORS_H_
+#define OPERATORS_H_
+
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+    // Note status needs to be aligned with graph_status
+    enum tosa_status_t
+    {
+        tosa_status_valid         = 0,
+        tosa_status_unpredictable = 1,
+        tosa_status_error         = 2
+    };
+
+    enum tosa_mode_t
+    {
+        tosa_mode_unknown  = 0,
+        tosa_mode_nearest  = 1,
+        tosa_mode_bilinear = 2,
+        tosa_mode_min      = 3,
+        tosa_mode_max      = 4
+    };
+
+    enum tosa_datatype_t
+    {
+        tosa_datatype_bf16_t   = 0,
+        tosa_datatype_bool_t   = 1,
+        tosa_datatype_fp16_t   = 2,
+        tosa_datatype_fp32_t   = 3,
+        tosa_datatype_int16_t  = 4,
+        tosa_datatype_int32_t  = 5,
+        tosa_datatype_int48_t  = 6,
+        tosa_datatype_int4_t   = 7,
+        tosa_datatype_int8_t   = 8,
+        tosa_datatype_uint16_t = 9,
+        tosa_datatype_uint8_t  = 10,
+    };
+
+    struct tosa_tensor_t
+    {
+        int32_t* shape;
+        int32_t num_dims;
+        tosa_datatype_t data_type;
+        uint8_t* data;
+        size_t size;
+    };
+
+    tosa_status_t tosa_run_argmax(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_avg_pool2d(tosa_tensor_t client_input,
+                                      const int32_t client_kernel[2],
+                                      const int32_t client_stride[2],
+                                      const int32_t client_pad[4],
+                                      const int32_t client_input_zp,
+                                      const int32_t client_output_zp,
+                                      tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_conv2d(tosa_tensor_t client_input,
+                                  tosa_tensor_t client_weight,
+                                  tosa_tensor_t client_bias,
+                                  const int32_t client_pad[4],
+                                  const int32_t client_stride[2],
+                                  const int32_t client_dilation[2],
+                                  const int32_t client_input_zp,
+                                  const int32_t client_weight_zp,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_conv3d(tosa_tensor_t client_input,
+                                  tosa_tensor_t client_weight,
+                                  tosa_tensor_t client_bias,
+                                  const int32_t client_pad[6],
+                                  const int32_t client_stride[3],
+                                  const int32_t client_dilation[3],
+                                  const int32_t client_input_zp,
+                                  const int32_t client_weight_zp,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_depthwise_conv2d(tosa_tensor_t client_input,
+                                            tosa_tensor_t client_weight,
+                                            tosa_tensor_t client_bias,
+                                            const int32_t client_pad[4],
+                                            const int32_t client_stride[2],
+                                            const int32_t client_dilation[2],
+                                            const int32_t client_input_zp,
+                                            const int32_t client_weight_zp,
+                                            tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_fully_connected(tosa_tensor_t client_input,
+                                           const int32_t client_input_zp,
+                                           const int32_t client_weight_zp,
+                                           tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_matmul(tosa_tensor_t client_a,
+                                  tosa_tensor_t client_b,
+                                  const int32_t client_a_zp,
+                                  const int32_t client_b_zp,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_max_pool2d(tosa_tensor_t client_input,
+                                      const int32_t client_kernel[2],
+                                      const int32_t client_stride[2],
+                                      const int32_t client_pad[4],
+                                      const int32_t client_input_zp,
+                                      const int32_t client_output_zp,
+                                      tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_transpose_conv2d(tosa_tensor_t client_input,
+                                            tosa_tensor_t client_weight,
+                                            tosa_tensor_t client_bias,
+                                            const int32_t client_out_pad[4],
+                                            const int32_t client_stride[2],
+                                            const int32_t client_out_shape[4],
+                                            const int32_t client_input_zp,
+                                            const int32_t client_weight_zp,
+                                            const int32_t client_pad_len,
+                                            const int32_t client_pad[],
+                                            const int32_t client_dilation_len,
+                                            const int32_t client_dilation[],
+                                            tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_clamp(tosa_tensor_t client_input,
+                                 const int32_t client_min_int,
+                                 const int32_t client_max_int,
+                                 const float client_min_fp,
+                                 const float client_max_fp,
+                                 tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_sigmoid(tosa_tensor_t client_input, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_tanh(tosa_tensor_t client_input, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_add(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_arithmetic_right_shift(tosa_tensor_t client_input1,
+                                                  tosa_tensor_t client_input2,
+                                                  const bool client_round,
+                                                  tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_bitwise_and(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_bitwise_or(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_bitwise_xor(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_intdiv(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_logical_and(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_logical_left_shift(tosa_tensor_t client_input1,
+                                              tosa_tensor_t client_input2,
+                                              tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_logical_right_shift(tosa_tensor_t client_input1,
+                                               tosa_tensor_t client_input2,
+                                               tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_logical_or(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_logical_xor(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_maximum(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_minimum(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_mul(tosa_tensor_t client_input1,
+                               tosa_tensor_t client_input2,
+                               const uint8_t client_shift,
+                               tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_pow(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_sub(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_table(tosa_tensor_t client_input,
+                                 const int32_t client_table_len,
+                                 const int16_t client_table[],
+                                 tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_abs(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_bitwise_not(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_ceil(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_clz(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_exp(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_floor(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_log(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_logical_not(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_negate(tosa_tensor_t client_input1,
+                                  const int32_t client_input1_zp,
+                                  const int32_t client_output_zp,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_reciprocal(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_rsqrt(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_select(tosa_tensor_t client_input1,
+                                  tosa_tensor_t client_input2,
+                                  tosa_tensor_t client_input3,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_equal(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_greater(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_greater_equal(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_all(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_any(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_max(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_min(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_product(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_reduce_sum(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_concat(tosa_tensor_t client_input1, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_pad(tosa_tensor_t client_input1,
+                               const int32_t client_padding_len,
+                               const int32_t client_padding[],
+                               const int32_t client_pad_const_int,
+                               const float client_pad_const_fp,
+                               tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_reshape(tosa_tensor_t client_input1,
+                                   const int32_t client_new_shape_len,
+                                   const int32_t client_new_shape[],
+                                   tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_reverse(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_slice(tosa_tensor_t client_input1,
+                                 const int32_t client_start_len,
+                                 const int32_t client_start[],
+                                 const int32_t client_size_len,
+                                 const int32_t client_size[],
+                                 tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_tile(tosa_tensor_t client_input1,
+                                const int32_t client_multiplies_len,
+                                const int32_t client_multiplies[],
+                                const int32_t client_multiples_len,
+                                const int32_t client_multiples[],
+                                tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_transpose(tosa_tensor_t client_input1,
+                                     const int32_t client_perms_len,
+                                     const int32_t client_perms[],
+                                     tosa_tensor_t client_output);
+
+    tosa_status_t
+        tosa_run_gather(tosa_tensor_t client_values, tosa_tensor_t client_indices, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_scatter(tosa_tensor_t client_values_in,
+                                   tosa_tensor_t client_indices,
+                                   tosa_tensor_t client_input,
+                                   tosa_tensor_t client_values_out);
+
+    tosa_status_t tosa_run_resize(tosa_tensor_t client_input,
+                                  const int16_t client_scale[4],
+                                  const int16_t client_offset[2],
+                                  const int16_t client_border[2],
+                                  const tosa_mode_t client_mode,
+                                  tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_cast(tosa_tensor_t client_input, tosa_tensor_t client_output);
+
+    tosa_status_t tosa_run_rescale(tosa_tensor_t client_input,
+                                   tosa_tensor_t client_output,
+                                   const int32_t client_input_zp,
+                                   const int32_t client_output_zp,
+                                   const int32_t client_multiplier_len,
+                                   const int32_t client_multiplier[],
+                                   const int32_t client_shift_len,
+                                   const uint8_t client_shift[],
+                                   const bool client_scale32,
+                                   const bool client_double_round,
+                                   const bool client_per_channel);
+
+    tosa_status_t tosa_run_identity(tosa_tensor_t client_input1, tosa_tensor_t client_output);
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif    // OPERATORS_H_
\ No newline at end of file
diff --git a/reference_model/src/array_proxy.h b/reference_model/src/array_proxy.h
new file mode 100644
index 0000000..f6b3105
--- /dev/null
+++ b/reference_model/src/array_proxy.h
@@ -0,0 +1,98 @@
+
+// Copyright (c) 2022, ARM Limited.
+//
+//    Licensed under the Apache License, Version 2.0 (the "License");
+//    you may not use this file except in compliance with the License.
+//    You may obtain a copy of the License at
+//
+//         http://www.apache.org/licenses/LICENSE-2.0
+//
+//    Unless required by applicable law or agreed to in writing, software
+//    distributed under the License is distributed on an "AS IS" BASIS,
+//    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//    See the License for the specific language governing permissions and
+//    limitations under the License.
+
+#ifndef ARRAY_PROXY_H_
+#define ARRAY_PROXY_H_
+
+#include <cstddef>
+#include <type_traits>
+
+template <typename T>
+class ArrayProxy
+{
+public:
+    ArrayProxy(size_t n, T* ptr) noexcept
+        : _n(n)
+        , _ptr(ptr)
+    {}
+
+    template <typename U = T, std::enable_if_t<std::is_const<U>::value, int> = 0>
+    ArrayProxy(size_t n, typename std::remove_const_t<T>* ptr) noexcept
+        : _n(n)
+        , _ptr(ptr)
+    {}
+
+    template <std::size_t S>
+    ArrayProxy(T (&ptr)[S]) noexcept
+        : _n(S)
+        , _ptr(ptr)
+    {}
+
+    template <std::size_t S, typename U = T, std::enable_if_t<std::is_const<U>::value, int> = 0>
+    ArrayProxy(typename std::remove_const_t<T> (&ptr)[S]) noexcept
+        : _n(S)
+        , _ptr(ptr)
+    {}
+
+    template <typename O,
+              std::enable_if_t<std::is_convertible_v<decltype(std::declval<O>().data()), T*> &&
+                                   std::is_convertible_v<decltype(std::declval<O>().size()), std::size_t>,
+                               int> = 0>
+    ArrayProxy(O& obj) noexcept
+        : _n(obj.size())
+        , _ptr(obj.data())
+    {}
+
+    size_t size() const noexcept
+    {
+        return _n;
+    }
+
+    T* data() const noexcept
+    {
+        return _ptr;
+    }
+
+    bool empty() const noexcept
+    {
+        return _n == 0;
+    }
+
+    const T* begin() const noexcept
+    {
+        return _ptr;
+    }
+
+    const T* end() const noexcept
+    {
+        return _ptr + _n;
+    }
+
+    T& operator[](size_t idx) noexcept
+    {
+        return *(_ptr + idx);
+    }
+
+    const T& operator[](size_t idx) const noexcept
+    {
+        return *(_ptr + idx);
+    }
+
+private:
+    size_t _n;
+    T* _ptr;
+};
+
+#endif    // ARRAY_PROXY_H_
diff --git a/reference_model/src/model_runner.cc b/reference_model/src/model_runner.cc
index 5c086e6..62d6ce6 100644
--- a/reference_model/src/model_runner.cc
+++ b/reference_model/src/model_runner.cc
@@ -55,7 +55,12 @@ GraphStatus IModelRunner::run()
 template <typename T>
 int IModelRunner::setInput(std::string input_name, std::vector<T>& vals)
 {
-    return model_runner_impl->setInput<T>(input_name, vals);
+    return model_runner_impl->setInput<T>(input_name, ArrayProxy(vals.size(), vals.data()));
+}
+
+int IModelRunner::setInput(std::string input_name, uint8_t* raw_ptr, size_t size)
+{
+    return model_runner_impl->setInput(input_name, raw_ptr, size);
 }
 
 template <typename T>
@@ -64,6 +69,11 @@ std::vector<T> IModelRunner::getOutput(std::string output_name)
     return model_runner_impl->getOutput<T>(output_name);
 }
 
+int IModelRunner::getOutput(std::string output_name, uint8_t* raw_ptr, size_t size)
+{
+    return model_runner_impl->getOutput(output_name, raw_ptr, size);
+}
+
 // Template explicit specialization
 template int IModelRunner::setInput<float>(std::string input_name, std::vector<float>& vals);
 template int IModelRunner::setInput<half_float::half>(std::string input_name, std::vector<half_float::half>& vals);
diff --git a/reference_model/src/model_runner_impl.cc b/reference_model/src/model_runner_impl.cc
index 8427150..1109dd6 100644
--- a/reference_model/src/model_runner_impl.cc
+++ b/reference_model/src/model_runner_impl.cc
@@ -45,42 +45,12 @@ void ModelRunnerImpl::setFuncDebug(func_debug_t& func_debug)
 GraphStatus ModelRunnerImpl::initialize(TosaSerializationHandler& serialization_handler)
 {
     validateTosaVersion(serialization_handler);
+    return initialize(serialization_handler.GetMainBlock(), &serialization_handler);
+}
 
-    // Make nullptr in case ModelRunnerImpl is being initialized again with a different graph.
-    _main_gt = nullptr;
-    _main_gt = new SubgraphTraverser(serialization_handler.GetMainBlock(), &serialization_handler);
-
-    if(_main_gt == nullptr)
-    {
-        WARNING("An error occurred when generating main graph traverser.");
-        return GraphStatus::TOSA_ERROR;
-    }
-
-    if (_main_gt->initializeGraph())
-    {
-        WARNING("Unable to initialize main graph traverser.");
-        return _main_gt->getGraphStatus();
-    }
-
-    if (_main_gt->linkTensorsAndNodes())
-    {
-        WARNING("Failed to link tensors and nodes");
-        return _main_gt->getGraphStatus();
-    }
-
-    if (_main_gt->validateGraph())
-    {
-        WARNING("Failed to validate graph.");
-        return _main_gt->getGraphStatus();
-    }
-
-    if (_main_gt->allocateTensor())
-    {
-        WARNING("Failed to allocate tensor.");
-        return _main_gt->getGraphStatus();
-    }
-
-    return _main_gt->getGraphStatus();
+GraphStatus ModelRunnerImpl::initialize(TosaSerializationBasicBlock& bb)
+{
+    return initialize(&bb, nullptr);
 }
 
 GraphStatus ModelRunnerImpl::run()
@@ -156,7 +126,7 @@ done:
 }
 
 template <typename T>
-int ModelRunnerImpl::setInput(std::string input_name, std::vector<T>& vals)
+int ModelRunnerImpl::setInput(std::string input_name, ArrayProxy<T> vals)
 {
     if (_main_gt == nullptr)
     {
@@ -197,6 +167,44 @@ int ModelRunnerImpl::setInput(std::string input_name, std::vector<T>& vals)
     return 0;
 }
 
+int ModelRunnerImpl::setInput(std::string input_name, uint8_t* raw_ptr, size_t size)
+{
+    if (_main_gt == nullptr)
+    {
+        FATAL_ERROR("ModelRunner hasn't been initialized, please invoke initialize() before setInput()");
+    }
+
+    Tensor* tensor;
+    tensor = _main_gt->getInputTensorByName(input_name);
+
+    if (!tensor)
+    {
+        WARNING("Unable to find input tensor %s", input_name.c_str());
+        return 1;
+    }
+
+    int status = 0;
+    switch (tensor->getDtype())
+    {
+        case DType::DType_FP16: {
+            auto typed_ptr     = reinterpret_cast<half_float::half*>(raw_ptr);
+            const int elements = size / sizeof(half_float::half);
+            status             = setInput(input_name, ArrayProxy(elements, typed_ptr));
+            break;
+        }
+        case DType::DType_FP32: {
+            auto typed_ptr     = reinterpret_cast<float*>(raw_ptr);
+            const int elements = size / sizeof(float);
+            status             = setInput(input_name, ArrayProxy(elements, typed_ptr));
+            break;
+        }
+        default:
+            status = 1;
+    }
+
+    return status;
+}
+
 template <typename T>
 std::vector<T> ModelRunnerImpl::getOutput(std::string output_name)
 {
@@ -216,7 +224,7 @@ std::vector<T> ModelRunnerImpl::getOutput(std::string output_name)
 
     std::vector<T> outputs(tensor->getElementCount());
 
-    if (tensor->writeToVector(outputs))
+    if (tensor->writeToVector(ArrayProxy<T>(outputs)))
     {
         WARNING("Unable to convert output tensor %s to vector", tensor->getName().c_str());
         return std::vector<T>();
@@ -225,6 +233,92 @@ std::vector<T> ModelRunnerImpl::getOutput(std::string output_name)
     return outputs;
 }
 
+int ModelRunnerImpl::getOutput(std::string output_name, uint8_t* raw_ptr, size_t size)
+{
+    if (_main_gt == nullptr)
+    {
+        FATAL_ERROR("ModelRunner hasn't been initialized, please invoke initialize() and run() before getOutput()");
+    }
+
+    Tensor* tensor;
+    tensor = _main_gt->getOutputTensorByName(output_name);
+
+    if (!tensor)
+    {
+        WARNING("Unable to find output tensor %s", output_name.c_str());
+        return 1;
+    }
+
+    int status = 0;
+    switch (tensor->getDtype())
+    {
+        case DType::DType_FP16: {
+            auto typed_ptr     = reinterpret_cast<half_float::half*>(raw_ptr);
+            const int elements = size / sizeof(half_float::half);
+            status             = tensor->writeToVector(ArrayProxy(elements, typed_ptr));
+            break;
+        }
+        case DType::DType_FP32: {
+            auto typed_ptr     = reinterpret_cast<float*>(raw_ptr);
+            const int elements = size / sizeof(float);
+            status             = tensor->writeToVector(ArrayProxy(elements, typed_ptr));
+            break;
+        }
+        default:
+            status = 1;
+    }
+    if (status)
+    {
+        WARNING("Unable to convert output tensor %s to vector", tensor->getName().c_str());
+        return 1;
+    }
+
+    return 0;
+}
+
+GraphStatus ModelRunnerImpl::initialize(TosaSerializationBasicBlock* bb,
+                                        TosaSerializationHandler* serialization_handler)
+{
+    if (serialization_handler != nullptr)
+        validateTosaVersion(*serialization_handler);
+
+    // Make nullptr in case ModelRunnerImpl is being initialized again with a different graph.
+    _main_gt = nullptr;
+    _main_gt = new SubgraphTraverser(bb, serialization_handler);
+
+    if (_main_gt == nullptr)
+    {
+        WARNING("An error occurred when generating main graph traverser.");
+        return GraphStatus::TOSA_ERROR;
+    }
+
+    if (_main_gt->initializeGraph())
+    {
+        WARNING("Unable to initialize main graph traverser.");
+        return _main_gt->getGraphStatus();
+    }
+
+    if (_main_gt->linkTensorsAndNodes())
+    {
+        WARNING("Failed to link tensors and nodes");
+        return _main_gt->getGraphStatus();
+    }
+
+    if (_main_gt->validateGraph())
+    {
+        WARNING("Failed to validate graph.");
+        return _main_gt->getGraphStatus();
+    }
+
+    if (_main_gt->allocateTensor())
+    {
+        WARNING("Failed to allocate tensor.");
+        return _main_gt->getGraphStatus();
+    }
+
+    return _main_gt->getGraphStatus();
+}
+
 void ModelRunnerImpl::validateTosaVersion(TosaSerializationHandler& serialization_handler)
 {
     TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR,
@@ -266,11 +360,11 @@ void ModelRunnerImpl::checkGraphStatus(SubgraphTraverser& main_gt)
 }
 
 // Template explicit specialization
-template int ModelRunnerImpl::setInput<float>(std::string input_name, std::vector<float>& vals);
-template int ModelRunnerImpl::setInput<half_float::half>(std::string input_name, std::vector<half_float::half>& vals);
-template int ModelRunnerImpl::setInput<int32_t>(std::string input_name, std::vector<int32_t>& vals);
-template int ModelRunnerImpl::setInput<int64_t>(std::string input_name, std::vector<int64_t>& vals);
-template int ModelRunnerImpl::setInput<unsigned char>(std::string input_name, std::vector<unsigned char>& vals);
+template int ModelRunnerImpl::setInput<float>(std::string input_name, ArrayProxy<float> vals);
+template int ModelRunnerImpl::setInput<half_float::half>(std::string input_name, ArrayProxy<half_float::half> vals);
+template int ModelRunnerImpl::setInput<int32_t>(std::string input_name, ArrayProxy<int32_t> vals);
+template int ModelRunnerImpl::setInput<int64_t>(std::string input_name, ArrayProxy<int64_t> vals);
+template int ModelRunnerImpl::setInput<unsigned char>(std::string input_name, ArrayProxy<unsigned char> vals);
 
 template std::vector<float> ModelRunnerImpl::getOutput<float>(std::string output_name);
 template std::vector<half_float::half> ModelRunnerImpl::getOutput<half_float::half>(std::string output_name);
diff --git a/reference_model/src/model_runner_impl.h b/reference_model/src/model_runner_impl.h
index f26c484..b43370c 100644
--- a/reference_model/src/model_runner_impl.h
+++ b/reference_model/src/model_runner_impl.h
@@ -20,6 +20,7 @@
 #include "graph_status.h"
 #include "version.h"
 
+#include "array_proxy.h"
 #include "ops/op_factory.h"
 #include "subgraph_traverser.h"
 #include "tosa_serialization_handler.h"
@@ -42,14 +43,17 @@ public:
     void setFuncConfig(func_config_t& func_config);
     void setFuncDebug(func_debug_t& func_debug);
 
+    GraphStatus initialize(TosaSerializationBasicBlock& bb);
     GraphStatus initialize(TosaSerializationHandler& serialization_handler);
     GraphStatus run();
 
     template <typename T>
-    int setInput(std::string input_name, std::vector<T>& vals);
+    int setInput(std::string input_name, ArrayProxy<T> vals);
+    int setInput(std::string input_name, uint8_t* raw_ptr, size_t size);
 
     template <typename T>
     std::vector<T> getOutput(std::string output_name);
+    int getOutput(std::string output_name, uint8_t* ptr, size_t size);
 
 private:
     SubgraphTraverser* _main_gt = nullptr;
@@ -57,6 +61,7 @@ private:
     // Used to determine if all input tensors have been set correctly.
     uint32_t n_input_tensors = 0;
 
+    GraphStatus initialize(TosaSerializationBasicBlock* bb, TosaSerializationHandler* serialization_handler);
     void validateTosaVersion(TosaSerializationHandler& serialization_handler);
     void checkGraphStatus(SubgraphTraverser& main_gt);
 };
diff --git a/reference_model/src/operators.cc b/reference_model/src/operators.cc
new file mode 100644
index 0000000..dfad9b8
--- /dev/null
+++ b/reference_model/src/operators.cc
@@ -0,0 +1,2325 @@
+
+// Copyright (c) 2022, ARM Limited.
+//
+//    Licensed under the Apache License, Version 2.0 (the "License");
+//    you may not use this file except in compliance with the License.
+//    You may obtain a copy of the License at
+//
+//         http://www.apache.org/licenses/LICENSE-2.0
+//
+//    Unless required by applicable law or agreed to in writing, software
+//    distributed under the License is distributed on an "AS IS" BASIS,
+//    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//    See the License for the specific language governing permissions and
+//    limitations under the License.
+
+// THIS FILE IS GENERATED. DO NOT EDIT!
+// See scripts/operator_api/generate_api.py
+
+#include "operators.h"
+#include "model_runner_impl.h"
+#include "ops/op_factory.h"
+
+#define TOSA_RETURN_ON_ERROR(status)                                                                                   \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (status != 0)                                                                                               \
+        {                                                                                                              \
+            return tosa_status_error;                                                                                  \
+        }                                                                                                              \
+    } while (false)
+
+#define TOSA_RETURN_ON_GRAPH_STATUS_ERROR(status)                                                                      \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (status != GraphStatus::TOSA_VALID)                                                                         \
+        {                                                                                                              \
+            auto ustatus = static_cast<std::underlying_type_t<GraphStatus>>(status);                                   \
+            return static_cast<tosa_status_t>(ustatus);                                                                \
+        }                                                                                                              \
+    } while (false)
+
+namespace
+{
+
+tosa::DType translate_client_datatype(tosa_datatype_t type)
+{
+    switch (type)
+    {
+        case tosa_datatype_fp16_t:
+            return tosa::DType::DType_FP16;
+        case tosa_datatype_fp32_t:
+            return tosa::DType::DType_FP32;
+        default:
+            return tosa::DType::DType_UNKNOWN;
+    }
+};
+
+tosa::TosaSerializationTensor* translate_client_tensor(tosa_tensor_t& tensor, const std::string& name)
+{
+    std::vector<int32_t> shape(tensor.shape, tensor.shape + tensor.num_dims);
+    return new tosa::TosaSerializationTensor(name, shape, translate_client_datatype(tensor.data_type), {});
+}
+
+tosa::ResizeMode translate_client_tosa_mode(tosa_mode_t mode)
+{
+    switch (mode)
+    {
+        case tosa_mode_nearest:
+            return tosa::ResizeMode_NEAREST;
+        case tosa_mode_max:
+        case tosa_mode_bilinear:
+            return tosa::ResizeMode_BILINEAR;
+        default:
+            return tosa::ResizeMode_UNKNOWN;
+    }
+}
+
+}    // namespace
+
+extern "C"
+{
+
+    tosa_status_t tosa_run_argmax(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_ARGMAX, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("argmax", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_avg_pool2d(tosa_tensor_t client_input,
+                                      const int32_t client_kernel[2],
+                                      const int32_t client_stride[2],
+                                      const int32_t client_pad[4],
+                                      const int32_t client_input_zp,
+                                      const int32_t client_output_zp,
+                                      tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[4]);
+        const std::vector<int32_t> kernel(&client_kernel[0], &client_kernel[2]);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[2]);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t output_zp       = client_output_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaPoolAttribute attr(pad, kernel, stride, input_zp, output_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_AVG_POOL2D, tosa::Attribute::Attribute_PoolAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("avg_pool2d", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_conv2d(tosa_tensor_t client_input,
+                                  tosa_tensor_t client_weight,
+                                  tosa_tensor_t client_bias,
+                                  const int32_t client_pad[4],
+                                  const int32_t client_stride[2],
+                                  const int32_t client_dilation[2],
+                                  const int32_t client_input_zp,
+                                  const int32_t client_weight_zp,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[4]);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[2]);
+        const std::vector<int32_t> dilation(&client_dilation[0], &client_dilation[2]);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t weight_zp       = client_weight_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaConvAttribute attr(pad, stride, dilation, input_zp, weight_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* weight = translate_client_tensor(client_weight, "weight");
+        tosa::TosaSerializationTensor* bias   = translate_client_tensor(client_bias, "bias");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CONV2D, tosa::Attribute::Attribute_ConvAttribute,
+                                                      &attr, { input->GetName(), weight->GetName(), bias->GetName() },
+                                                      { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("conv2d", { op }, { input, weight, bias, output },
+                                                { input->GetName(), weight->GetName(), bias->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(weight->GetName(), client_weight.data, client_weight.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(bias->GetName(), client_bias.data, client_bias.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_conv3d(tosa_tensor_t client_input,
+                                  tosa_tensor_t client_weight,
+                                  tosa_tensor_t client_bias,
+                                  const int32_t client_pad[6],
+                                  const int32_t client_stride[3],
+                                  const int32_t client_dilation[3],
+                                  const int32_t client_input_zp,
+                                  const int32_t client_weight_zp,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[6]);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[3]);
+        const std::vector<int32_t> dilation(&client_dilation[0], &client_dilation[3]);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t weight_zp       = client_weight_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaConvAttribute attr(pad, stride, dilation, input_zp, weight_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* weight = translate_client_tensor(client_weight, "weight");
+        tosa::TosaSerializationTensor* bias   = translate_client_tensor(client_bias, "bias");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CONV3D, tosa::Attribute::Attribute_ConvAttribute,
+                                                      &attr, { input->GetName(), weight->GetName(), bias->GetName() },
+                                                      { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("conv3d", { op }, { input, weight, bias, output },
+                                                { input->GetName(), weight->GetName(), bias->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(weight->GetName(), client_weight.data, client_weight.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(bias->GetName(), client_bias.data, client_bias.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_depthwise_conv2d(tosa_tensor_t client_input,
+                                            tosa_tensor_t client_weight,
+                                            tosa_tensor_t client_bias,
+                                            const int32_t client_pad[4],
+                                            const int32_t client_stride[2],
+                                            const int32_t client_dilation[2],
+                                            const int32_t client_input_zp,
+                                            const int32_t client_weight_zp,
+                                            tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[4]);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[2]);
+        const std::vector<int32_t> dilation(&client_dilation[0], &client_dilation[2]);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t weight_zp       = client_weight_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaConvAttribute attr(pad, stride, dilation, input_zp, weight_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* weight = translate_client_tensor(client_weight, "weight");
+        tosa::TosaSerializationTensor* bias   = translate_client_tensor(client_bias, "bias");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(
+            tosa::Op::Op_DEPTHWISE_CONV2D, tosa::Attribute::Attribute_ConvAttribute, &attr,
+            { input->GetName(), weight->GetName(), bias->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("depthwise_conv2d", { op }, { input, weight, bias, output },
+                                                { input->GetName(), weight->GetName(), bias->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(weight->GetName(), client_weight.data, client_weight.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(bias->GetName(), client_bias.data, client_bias.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_fully_connected(tosa_tensor_t client_input,
+                                           const int32_t client_input_zp,
+                                           const int32_t client_weight_zp,
+                                           tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const int32_t input_zp        = client_input_zp;
+        const int32_t weight_zp       = client_weight_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaFullyConnectedAttribute attr(input_zp, weight_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_FULLY_CONNECTED,
+                                                      tosa::Attribute::Attribute_FullyConnectedAttribute, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("fully_connected", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_matmul(tosa_tensor_t client_a,
+                                  tosa_tensor_t client_b,
+                                  const int32_t client_a_zp,
+                                  const int32_t client_b_zp,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const int32_t a_zp            = client_a_zp;
+        const int32_t b_zp            = client_b_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaMatMulAttribute attr(a_zp, b_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* a      = translate_client_tensor(client_a, "a");
+        tosa::TosaSerializationTensor* b      = translate_client_tensor(client_b, "b");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_MATMUL, tosa::Attribute::Attribute_MatMulAttribute,
+                                                      &attr, { a->GetName(), b->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("matmul", { op }, { a, b, output }, { a->GetName(), b->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(a->GetName(), client_a.data, client_a.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(b->GetName(), client_b.data, client_b.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_max_pool2d(tosa_tensor_t client_input,
+                                      const int32_t client_kernel[2],
+                                      const int32_t client_stride[2],
+                                      const int32_t client_pad[4],
+                                      const int32_t client_input_zp,
+                                      const int32_t client_output_zp,
+                                      tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[4]);
+        const std::vector<int32_t> kernel(&client_kernel[0], &client_kernel[2]);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[2]);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t output_zp       = client_output_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaPoolAttribute attr(pad, kernel, stride, input_zp, output_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_MAX_POOL2D, tosa::Attribute::Attribute_PoolAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("max_pool2d", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_transpose_conv2d(tosa_tensor_t client_input,
+                                            tosa_tensor_t client_weight,
+                                            tosa_tensor_t client_bias,
+                                            const int32_t client_out_pad[4],
+                                            const int32_t client_stride[2],
+                                            const int32_t client_out_shape[4],
+                                            const int32_t client_input_zp,
+                                            const int32_t client_weight_zp,
+                                            const int32_t client_pad_len,
+                                            const int32_t client_pad[],
+                                            const int32_t client_dilation_len,
+                                            const int32_t client_dilation[],
+                                            tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> pad(&client_pad[0], &client_pad[0] + client_pad_len);
+        const std::vector<int32_t> stride(&client_stride[0], &client_stride[2]);
+        const std::vector<int32_t> dilation(&client_dilation[0], &client_dilation[0] + client_dilation_len);
+        const int32_t input_zp        = client_input_zp;
+        const int32_t weight_zp       = client_weight_zp;
+        const tosa::DType accum_dtype = tosa::DType::DType_FP32;
+        TosaConvAttribute attr(pad, stride, dilation, input_zp, weight_zp, accum_dtype);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* weight = translate_client_tensor(client_weight, "weight");
+        tosa::TosaSerializationTensor* bias   = translate_client_tensor(client_bias, "bias");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(
+            tosa::Op::Op_TRANSPOSE_CONV2D, tosa::Attribute::Attribute_ConvAttribute, &attr,
+            { input->GetName(), weight->GetName(), bias->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("transpose_conv2d", { op }, { input, weight, bias, output },
+                                                { input->GetName(), weight->GetName(), bias->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(weight->GetName(), client_weight.data, client_weight.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(bias->GetName(), client_bias.data, client_bias.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_clamp(tosa_tensor_t client_input,
+                                 const int32_t client_min_int,
+                                 const int32_t client_max_int,
+                                 const float client_min_fp,
+                                 const float client_max_fp,
+                                 tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const int32_t min_int = client_min_int;
+        const int32_t max_int = client_max_int;
+        const float min_fp    = client_min_fp;
+        const float max_fp    = client_max_fp;
+        TosaClampAttribute attr(min_int, max_int, min_fp, max_fp);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CLAMP, tosa::Attribute::Attribute_ClampAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("clamp", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_sigmoid(tosa_tensor_t client_input, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_SIGMOID, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("sigmoid", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_tanh(tosa_tensor_t client_input, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_TANH, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("tanh", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_add(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_ADD, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("add", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_arithmetic_right_shift(tosa_tensor_t client_input1,
+                                                  tosa_tensor_t client_input2,
+                                                  const bool client_round,
+                                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const bool round = client_round;
+        TosaArithmeticRightShiftAttribute attr(round);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_ARITHMETIC_RIGHT_SHIFT,
+                                                      tosa::Attribute::Attribute_ArithmeticRightShiftAttribute, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("arithmetic_right_shift", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_bitwise_and(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_BITWISE_AND, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("bitwise_and", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_bitwise_or(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_BITWISE_OR, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("bitwise_or", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_bitwise_xor(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_BITWISE_XOR, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("bitwise_xor", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_intdiv(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_INTDIV, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("intdiv", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_logical_and(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_AND, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_and", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_logical_left_shift(tosa_tensor_t client_input1,
+                                              tosa_tensor_t client_input2,
+                                              tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op =
+            new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_LEFT_SHIFT, tosa::Attribute::Attribute_NONE, &attr,
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_left_shift", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_logical_right_shift(tosa_tensor_t client_input1,
+                                               tosa_tensor_t client_input2,
+                                               tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op =
+            new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_RIGHT_SHIFT, tosa::Attribute::Attribute_NONE,
+                                                &attr, { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_right_shift", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_logical_or(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_OR, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_or", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_logical_xor(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_XOR, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_xor", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_maximum(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_MAXIMUM, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("maximum", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_minimum(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_MINIMUM, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("minimum", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_mul(tosa_tensor_t client_input1,
+                               tosa_tensor_t client_input2,
+                               const uint8_t client_shift,
+                               tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const int32_t shift = client_shift;
+        TosaMulAttribute attr(shift);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_MUL, tosa::Attribute::Attribute_MulAttribute, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("mul", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_pow(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_POW, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("pow", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_sub(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_SUB, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("sub", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_table(tosa_tensor_t client_input,
+                                 const int32_t client_table_len,
+                                 const int16_t client_table[],
+                                 tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int16_t> table(&client_table[0], &client_table[0] + client_table_len);
+        TosaTableAttribute attr(table);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_TABLE, tosa::Attribute::Attribute_TableAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("table", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_abs(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_ABS, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("abs", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_bitwise_not(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_BITWISE_NOT, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("bitwise_not", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_ceil(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CEIL, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("ceil", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_clz(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CLZ, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("clz", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_exp(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_EXP, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("exp", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_floor(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_FLOOR, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("floor", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_log(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_LOG, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("log", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_logical_not(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_LOGICAL_NOT, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("logical_not", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_negate(tosa_tensor_t client_input1,
+                                  const int32_t client_input1_zp,
+                                  const int32_t client_output_zp,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const int32_t input1_zp = client_input1_zp;
+        const int32_t output_zp = client_output_zp;
+        TosaNegateAttribute attr(input1_zp, output_zp);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_NEGATE, tosa::Attribute::Attribute_NegateAttribute,
+                                                      &attr, { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("negate", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_reciprocal(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_RECIPROCAL, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reciprocal", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_rsqrt(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_RSQRT, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("rsqrt", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_select(tosa_tensor_t client_input1,
+                                  tosa_tensor_t client_input2,
+                                  tosa_tensor_t client_input3,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* input3 = translate_client_tensor(client_input3, "input3");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_SELECT, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName(), input3->GetName() },
+                                                      { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("select", { op }, { input1, input2, input3, output },
+                                                { input1->GetName(), input2->GetName(), input3->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input3->GetName(), client_input3.data, client_input3.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_equal(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_EQUAL, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("equal", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_greater(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_GREATER, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("greater", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_greater_equal(tosa_tensor_t client_input1, tosa_tensor_t client_input2, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* input2 = translate_client_tensor(client_input2, "input2");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op =
+            new tosa::TosaSerializationOperator(tosa::Op::Op_GREATER_EQUAL, tosa::Attribute::Attribute_NONE, &attr,
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("greater_equal", { op }, { input1, input2, output },
+                                                { input1->GetName(), input2->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input2->GetName(), client_input2.data, client_input2.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_all(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_ALL, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_all", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_any(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_ANY, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_any", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_max(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_MAX, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_max", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_min(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_MIN, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_min", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_product(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_PRODUCT, tosa::Attribute::Attribute_NONE,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_product", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_reduce_sum(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REDUCE_SUM, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reduce_sum", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_concat(tosa_tensor_t client_input1, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CONCAT, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("concat", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_pad(tosa_tensor_t client_input1,
+                               const int32_t client_padding_len,
+                               const int32_t client_padding[],
+                               const int32_t client_pad_const_int,
+                               const float client_pad_const_fp,
+                               tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> padding(&client_padding[0], &client_padding[0] + client_padding_len);
+        const int32_t pad_const_int = client_pad_const_int;
+        const float pad_const_fp    = client_pad_const_fp;
+        TosaPadAttribute attr(padding, pad_const_int, pad_const_fp);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_PAD, tosa::Attribute::Attribute_PadAttribute, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("pad", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_reshape(tosa_tensor_t client_input1,
+                                   const int32_t client_new_shape_len,
+                                   const int32_t client_new_shape[],
+                                   tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> new_shape(&client_new_shape[0], &client_new_shape[0] + client_new_shape_len);
+        TosaReshapeAttribute attr(new_shape);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_RESHAPE, tosa::Attribute::Attribute_ReshapeAttribute,
+                                                      &attr, { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reshape", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_reverse(tosa_tensor_t client_input, const int32_t client_axis, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_REVERSE, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("reverse", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_slice(tosa_tensor_t client_input1,
+                                 const int32_t client_start_len,
+                                 const int32_t client_start[],
+                                 const int32_t client_size_len,
+                                 const int32_t client_size[],
+                                 tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> start(&client_start[0], &client_start[0] + client_start_len);
+        const std::vector<int32_t> size(&client_size[0], &client_size[0] + client_size_len);
+        TosaSliceAttribute attr(start, size);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_SLICE, tosa::Attribute::Attribute_SliceAttribute,
+                                                      &attr, { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("slice", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_tile(tosa_tensor_t client_input1,
+                                const int32_t client_multiplies_len,
+                                const int32_t client_multiplies[],
+                                const int32_t client_multiples_len,
+                                const int32_t client_multiples[],
+                                tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> multiples(&client_multiples[0], &client_multiples[0] + client_multiples_len);
+        TosaTileAttribute attr(multiples);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_TILE, tosa::Attribute::Attribute_TileAttribute,
+                                                      &attr, { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("tile", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_transpose(tosa_tensor_t client_input1,
+                                     const int32_t client_perms_len,
+                                     const int32_t client_perms[],
+                                     tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int32_t> perms(&client_perms[0], &client_perms[0] + client_perms_len);
+        TosaTransposeAttribute attr(perms);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op =
+            new tosa::TosaSerializationOperator(tosa::Op::Op_TRANSPOSE, tosa::Attribute::Attribute_TransposeAttribute,
+                                                &attr, { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("transpose", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t
+        tosa_run_gather(tosa_tensor_t client_values, tosa_tensor_t client_indices, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* values  = translate_client_tensor(client_values, "values");
+        tosa::TosaSerializationTensor* indices = translate_client_tensor(client_indices, "indices");
+        tosa::TosaSerializationTensor* output  = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_GATHER, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { values->GetName(), indices->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("gather", { op }, { values, indices, output },
+                                                { values->GetName(), indices->GetName() }, { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(values->GetName(), client_values.data, client_values.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(indices->GetName(), client_indices.data, client_indices.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_scatter(tosa_tensor_t client_values_in,
+                                   tosa_tensor_t client_indices,
+                                   tosa_tensor_t client_input,
+                                   tosa_tensor_t client_values_out)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* values_in  = translate_client_tensor(client_values_in, "values_in");
+        tosa::TosaSerializationTensor* indices    = translate_client_tensor(client_indices, "indices");
+        tosa::TosaSerializationTensor* input      = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* values_out = translate_client_tensor(client_values_out, "values_out");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_SCATTER, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { values_in->GetName(), indices->GetName(), input->GetName() },
+                                                      { values_out->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("scatter", { op }, { values_in, indices, input, values_out },
+                                                { values_in->GetName(), indices->GetName(), input->GetName() },
+                                                { values_out->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(values_in->GetName(), client_values_in.data, client_values_in.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(indices->GetName(), client_indices.data, client_indices.size));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(values_out->GetName(), client_values_out.data, client_values_out.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_resize(tosa_tensor_t client_input,
+                                  const int16_t client_scale[4],
+                                  const int16_t client_offset[2],
+                                  const int16_t client_border[2],
+                                  const tosa_mode_t client_mode,
+                                  tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        const std::vector<int16_t> scale(&client_scale[0], &client_scale[4]);
+        const std::vector<int16_t> offset(&client_offset[0], &client_offset[2]);
+        const std::vector<int16_t> border(&client_border[0], &client_border[2]);
+        const ResizeMode mode = translate_client_tosa_mode(client_mode);
+        TosaResizeAttribute attr(scale, offset, border, mode);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_RESIZE, tosa::Attribute::Attribute_ResizeAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("resize", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_cast(tosa_tensor_t client_input, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_CAST, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("cast", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_rescale(tosa_tensor_t client_input,
+                                   tosa_tensor_t client_output,
+                                   const int32_t client_input_zp,
+                                   const int32_t client_output_zp,
+                                   const int32_t client_multiplier_len,
+                                   const int32_t client_multiplier[],
+                                   const int32_t client_shift_len,
+                                   const uint8_t client_shift[],
+                                   const bool client_scale32,
+                                   const bool client_double_round,
+                                   const bool client_per_channel)
+    {
+        // Create operator attributes
+        const int32_t input_zp  = client_input_zp;
+        const int32_t output_zp = client_output_zp;
+        const std::vector<int32_t> multiplier(&client_multiplier[0], &client_multiplier[0] + client_multiplier_len);
+        const std::vector<int32_t> shift(&client_shift[0], &client_shift[0] + client_shift_len);
+        const bool scale32      = client_scale32;
+        const bool double_round = client_double_round;
+        const bool per_channel  = client_per_channel;
+        TosaRescaleAttribute attr(input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel);
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input  = translate_client_tensor(client_input, "input");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_RESCALE, tosa::Attribute::Attribute_RescaleAttribute,
+                                                      &attr, { input->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("rescale", { op }, { input, output }, { input->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input->GetName(), client_input.data, client_input.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+    tosa_status_t tosa_run_identity(tosa_tensor_t client_input1, tosa_tensor_t client_output)
+    {
+        // Create operator attributes
+        TosaNoneAttribute attr;
+
+        // Create tensors
+        tosa::TosaSerializationTensor* input1 = translate_client_tensor(client_input1, "input1");
+        tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output");
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_IDENTITY, tosa::Attribute::Attribute_NONE, &attr,
+                                                      { input1->GetName() }, { output->GetName() });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("identity", { op }, { input1, output }, { input1->GetName() },
+                                                { output->GetName() });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        TOSA_RETURN_ON_ERROR(runner.setInput(input1->GetName(), client_input1.data, client_input1.size));
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        TOSA_RETURN_ON_ERROR(runner.getOutput(output->GetName(), client_output.data, client_output.size));
+
+        return tosa_status_valid;
+    }
+
+}    // extern "C"
\ No newline at end of file
diff --git a/reference_model/src/tensor.cc b/reference_model/src/tensor.cc
index e9598c4..3cf4aa0 100644
--- a/reference_model/src/tensor.cc
+++ b/reference_model/src/tensor.cc
@@ -15,6 +15,7 @@
 
 #include "tensor.h"
 #include "arith_util.h"
+#include "array_proxy.h"
 #include "half.hpp"
 
 using namespace TosaReference;
@@ -445,7 +446,7 @@ DEF_CTENSOR_COPY_VALUE_FROM(6, bool)
 
 #undef DEF_CTENSOR_COPY_VALUE_FROM
 
-int TosaReference::Tensor::readfromVector(const std::vector<float>& vals)
+int TosaReference::Tensor::readfromVector(const ArrayProxy<float> vals)
 {
     uint32_t elements = getElementCount();
     switch (getDtype())
@@ -470,7 +471,7 @@ int TosaReference::Tensor::readfromVector(const std::vector<float>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::readfromVector(const std::vector<half_float::half>& vals)
+int TosaReference::Tensor::readfromVector(const ArrayProxy<half_float::half> vals)
 {
     uint32_t elements = getElementCount();
     std::vector<float> tensor(elements);
@@ -502,7 +503,7 @@ int TosaReference::Tensor::readfromVector(const std::vector<half_float::half>& v
     return 0;
 }
 
-int TosaReference::Tensor::readfromVector(const std::vector<int32_t>& vals)
+int TosaReference::Tensor::readfromVector(const ArrayProxy<int32_t> vals)
 {
     uint32_t elements = getElementCount();
     switch (getDtype())
@@ -531,7 +532,7 @@ int TosaReference::Tensor::readfromVector(const std::vector<int32_t>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::readfromVector(const std::vector<int64_t>& vals)
+int TosaReference::Tensor::readfromVector(const ArrayProxy<int64_t> vals)
 {
     uint32_t elements = getElementCount();
     switch (getDtype())
@@ -555,7 +556,7 @@ int TosaReference::Tensor::readfromVector(const std::vector<int64_t>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::readfromVector(const std::vector<unsigned char>& vals)
+int TosaReference::Tensor::readfromVector(const ArrayProxy<unsigned char> vals)
 {
     uint32_t elements = getElementCount();
 
@@ -580,7 +581,7 @@ int TosaReference::Tensor::readfromVector(const std::vector<unsigned char>& vals
     return 0;
 }
 
-int TosaReference::Tensor::writeToVector(std::vector<float>& vals)
+int TosaReference::Tensor::writeToVector(ArrayProxy<float> vals)
 {
     uint32_t elements = getElementCount();
 
@@ -605,7 +606,7 @@ int TosaReference::Tensor::writeToVector(std::vector<float>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::writeToVector(std::vector<half_float::half>& vals)
+int TosaReference::Tensor::writeToVector(ArrayProxy<half_float::half> vals)
 {
     uint32_t elements = getElementCount();
     std::vector<float> tensor(elements);
@@ -636,7 +637,7 @@ int TosaReference::Tensor::writeToVector(std::vector<half_float::half>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::writeToVector(std::vector<int32_t>& vals)
+int TosaReference::Tensor::writeToVector(ArrayProxy<int32_t> vals)
 {
     uint32_t elements = getElementCount();
 
@@ -665,7 +666,7 @@ int TosaReference::Tensor::writeToVector(std::vector<int32_t>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::writeToVector(std::vector<int64_t>& vals)
+int TosaReference::Tensor::writeToVector(ArrayProxy<int64_t> vals)
 {
     uint32_t elements = getElementCount();
 
@@ -689,7 +690,7 @@ int TosaReference::Tensor::writeToVector(std::vector<int64_t>& vals)
     return 0;
 }
 
-int TosaReference::Tensor::writeToVector(std::vector<unsigned char>& vals)
+int TosaReference::Tensor::writeToVector(ArrayProxy<unsigned char> vals)
 {
     uint32_t elements = getElementCount();
 
diff --git a/reference_model/src/tensor.h b/reference_model/src/tensor.h
index a3ce4bb..08e865a 100644
--- a/reference_model/src/tensor.h
+++ b/reference_model/src/tensor.h
@@ -16,6 +16,7 @@
 #ifndef TOSA_REFERENCE_TENSOR_H
 #define TOSA_REFERENCE_TENSOR_H
 
+#include "array_proxy.h"
 #include "model_common.h"
 #include "ops/template_types.h"
 #include "tosa_generated.h"
@@ -228,17 +229,17 @@ public:
     virtual int writeToNpyFile(const char* filename) const;
     virtual int copyValueFrom(Tensor* tensor) = 0;
 
-    virtual int readfromVector(const std::vector<float>& vals);
-    virtual int readfromVector(const std::vector<half_float::half>& vals);
-    virtual int readfromVector(const std::vector<int32_t>& vals);
-    virtual int readfromVector(const std::vector<int64_t>& vals);
-    virtual int readfromVector(const std::vector<unsigned char>& vals);
-
-    virtual int writeToVector(std::vector<float>& vals);
-    virtual int writeToVector(std::vector<half_float::half>& vals);
-    virtual int writeToVector(std::vector<int32_t>& vals);
-    virtual int writeToVector(std::vector<int64_t>& vals);
-    virtual int writeToVector(std::vector<unsigned char>& vals);
+    virtual int readfromVector(const ArrayProxy<float> vals);
+    virtual int readfromVector(const ArrayProxy<half_float::half> vals);
+    virtual int readfromVector(const ArrayProxy<int32_t> vals);
+    virtual int readfromVector(const ArrayProxy<int64_t> vals);
+    virtual int readfromVector(const ArrayProxy<unsigned char> vals);
+
+    virtual int writeToVector(ArrayProxy<float> vals);
+    virtual int writeToVector(ArrayProxy<half_float::half> vals);
+    virtual int writeToVector(ArrayProxy<int32_t> vals);
+    virtual int writeToVector(ArrayProxy<int64_t> vals);
+    virtual int writeToVector(ArrayProxy<unsigned char> vals);
 
     const char* bool_to_str(bool in) const
     {
diff --git a/reference_model/test/model_runner_tests.cpp b/reference_model/test/model_runner_tests.cpp
index 8304bc7..bb57657 100644
--- a/reference_model/test/model_runner_tests.cpp
+++ b/reference_model/test/model_runner_tests.cpp
@@ -17,8 +17,11 @@
 #define DOCTEST_CONFIG_IMPLEMENT_WITH_MAIN
 #endif
 
-#include "model_runner.h"
 #include "general_utils.h"
+#include "model_runner.h"
+#include "operators.h"
+
+#include <numeric>
 
 // Remove conflicting REQUIRE definition between doctest and reference_model
 #undef REQUIRE
@@ -33,125 +36,343 @@ void compareOutput(std::vector<T>& tensor1, std::vector<T>& tensor2, size_t size
 {
     for (size_t i = 0; i < size; ++i)
     {
-        CHECK((tensor1[i] == tensor2[i]));
+        CHECK_MESSAGE(tensor1[i] == doctest::Approx(tensor2[i]), "");
     }
 }
 
 TEST_SUITE("model_runner")
 {
 
-TEST_CASE("simple_add_f32_test")
-{
-    std::string test_root(std::string(PROJECT_ROOT) + "../examples/test_add_1x4x4x4_f32/");
-    std::string tosa_model_file(test_root + "flatbuffer-tflite/test_add_1x4x4x4_f32.tosa");
-    std::string input0_file(test_root + "placeholder_0.npy");
-    std::string input1_file(test_root + "placeholder_1.npy");
-    std::string expected_output_file(test_root + "tflite_result.npy");
+    TEST_CASE("op_entry_add")
+    {
+        // Inputs/Outputs
+        tosa_datatype_t dt                = tosa_datatype_fp32_t;
+        std::vector<int32_t> input_shape  = { 2, 4, 4, 1 };
+        std::vector<int32_t> output_shape = { 2, 4, 4, 1 };
+        std::vector<float> srcData1(32, 4.0f);
+        std::vector<float> srcData2(32, 3.0f);
+        std::vector<float> dstData(32, 0.0f);
+
+        tosa_tensor_t input1;
+        input1.shape     = input_shape.data();
+        input1.num_dims  = input_shape.size();
+        input1.data_type = dt;
+        input1.data      = reinterpret_cast<uint8_t*>(srcData1.data());
+        input1.size      = srcData1.size() * sizeof(float);
+
+        tosa_tensor_t input2;
+        input2.shape     = input_shape.data();
+        input2.num_dims  = input_shape.size();
+        input2.data_type = dt;
+        input2.data      = reinterpret_cast<uint8_t*>(srcData2.data());
+        input2.size      = srcData2.size() * sizeof(float);
+
+        tosa_tensor_t output;
+        output.shape     = output_shape.data();
+        output.num_dims  = output_shape.size();
+        output.data_type = dt;
+        output.data      = reinterpret_cast<uint8_t*>(dstData.data());
+        output.size      = dstData.size() * sizeof(float);
+
+        // Execution
+        auto status = tosa_run_add(input1, input2, output);
+        CHECK((status == tosa_status_valid));
+
+        // Compare results
+        std::vector<float> expectedData(8, 7.0f);
+        compareOutput(dstData, expectedData, expectedData.size());
+    }
 
-    std::vector<std::string> input_names = { "TosaInput_0", "TosaInput_1" };
-    std::string output_name = "TosaOutput_0";
+    TEST_CASE("op_entry_avg_pool2d")
+    {
+        // Pool parameters
+        const int32_t kernel[2] = { 2, 2 };
+        const int32_t stride[2] = { 2, 2 };
+        const int32_t pad[4]    = { 0, 0, 0, 0 };
+
+        // Inputs/Outputs
+        tosa_datatype_t dt                = tosa_datatype_fp32_t;
+        std::vector<int32_t> input_shape  = { 2, 4, 4, 1 };
+        std::vector<int32_t> output_shape = { 2, 2, 2, 1 };
+        std::vector<float> srcData(32, 7.0f);
+        std::vector<float> dstData(8, 0.f);
+
+        tosa_tensor_t input;
+        input.shape     = input_shape.data();
+        input.num_dims  = input_shape.size();
+        input.data_type = dt;
+        input.data      = reinterpret_cast<uint8_t*>(srcData.data());
+        input.size      = srcData.size() * sizeof(float);
+
+        tosa_tensor_t output;
+        output.shape     = output_shape.data();
+        output.num_dims  = output_shape.size();
+        output.data_type = dt;
+        output.data      = reinterpret_cast<uint8_t*>(dstData.data());
+        output.size      = dstData.size() * sizeof(float);
+
+        // Execution
+        auto status = tosa_run_avg_pool2d(input, kernel, stride, pad, 0, 0, output);
+        CHECK((status == tosa_status_valid));
+
+        // Compare results
+        std::vector<float> expectedData(8, 7.0f);
+        compareOutput(dstData, expectedData, expectedData.size());
+    }
 
-    std::vector<int32_t> input0_shape = { 1, 4, 4, 1 };
-    std::vector<int32_t> input1_shape = { 1, 4, 4, 4 };
-    std::vector<int32_t> output_shape = { 1, 4, 4, 4 };
+    TEST_CASE("op_entry_conv2d")
+    {
+        // Conv parameters
+        const int32_t stride[2]   = { 1, 1 };
+        const int32_t pad[4]      = { 0, 0, 0, 0 };
+        const int32_t dilation[2] = { 1, 1 };
+
+        // Inputs/Outputs
+        tosa_datatype_t dt                = tosa_datatype_fp32_t;
+        std::vector<int32_t> input_shape  = { 1, 32, 32, 8 };
+        std::vector<int32_t> output_shape = { 1, 32, 32, 16 };
+        std::vector<int32_t> weight_shape = { 16, 1, 1, 8 };
+        std::vector<int32_t> bias_shape   = { 16 };
+        std::vector<float> srcData(32 * 32 * 8, 1.0f);
+        std::vector<float> dstData(32 * 32 * 16, 0.f);
+        std::vector<float> biasData(16, 0.f);
+        std::vector<float> weightData(16 * 8, 1.0f);
+
+        tosa_tensor_t input;
+        input.shape     = input_shape.data();
+        input.num_dims  = input_shape.size();
+        input.data_type = dt;
+        input.data      = reinterpret_cast<uint8_t*>(srcData.data());
+        input.size      = srcData.size() * sizeof(float);
+
+        tosa_tensor_t weight;
+        weight.shape     = weight_shape.data();
+        weight.num_dims  = weight_shape.size();
+        weight.data_type = dt;
+        weight.data      = reinterpret_cast<uint8_t*>(weightData.data());
+        weight.size      = weightData.size() * sizeof(float);
+
+        tosa_tensor_t bias;
+        bias.shape     = bias_shape.data();
+        bias.num_dims  = bias_shape.size();
+        bias.data_type = dt;
+        bias.data      = reinterpret_cast<uint8_t*>(biasData.data());
+        bias.size      = biasData.size() * sizeof(float);
+
+        tosa_tensor_t output;
+        output.shape     = output_shape.data();
+        output.num_dims  = output_shape.size();
+        output.data_type = dt;
+        output.data      = reinterpret_cast<uint8_t*>(dstData.data());
+        output.size      = dstData.size() * sizeof(float);
+
+        const int32_t input_zp  = 0;
+        const int32_t weight_zp = 0;
+
+        // Execution
+        auto status = tosa_run_conv2d(input, weight, bias, pad, stride, dilation, input_zp, weight_zp, output);
+        CHECK((status == tosa_status_valid));
+
+        // Compare results
+        std::vector<float> expectedData(32 * 32 * 16, 8.0f);
+        compareOutput(dstData, expectedData, expectedData.size());
+    }
 
-    std::vector<std::vector<float>> inputs(input_names.size());
-    std::vector<float> actual_outputs = { };
-    std::vector<float> expected_outputs = { };
+    TEST_CASE("op_entry_max_pool2d")
+    {
+        // Pool parameters
+        const int32_t kernel[2] = { 2, 2 };
+        const int32_t stride[2] = { 2, 2 };
+        const int32_t pad[4]    = { 0, 0, 0, 0 };
+
+        // Inputs/Outputs
+        tosa_datatype_t dt                = tosa_datatype_fp32_t;
+        std::vector<int32_t> input_shape  = { 2, 4, 4, 1 };
+        std::vector<int32_t> output_shape = { 2, 2, 2, 1 };
+        std::vector<float> srcData(32);
+        std::vector<float> dstData(8, 0.f);
+        std::iota(std::begin(srcData), std::end(srcData), 1);
+
+        tosa_tensor_t input;
+        input.shape     = input_shape.data();
+        input.num_dims  = input_shape.size();
+        input.data_type = dt;
+        input.data      = reinterpret_cast<uint8_t*>(srcData.data());
+        input.size      = srcData.size() * sizeof(float);
+
+        tosa_tensor_t output;
+        output.shape     = output_shape.data();
+        output.num_dims  = output_shape.size();
+        output.data_type = dt;
+        output.data      = reinterpret_cast<uint8_t*>(dstData.data());
+        output.size      = dstData.size() * sizeof(float);
+
+        // Execution
+        auto status = tosa_run_max_pool2d(input, kernel, stride, pad, 0, 0, output);
+        CHECK((status == tosa_status_valid));
+
+        // Compare results
+        std::vector<float> expectedData = { 6, 8, 14, 16, 22, 24, 30, 32 };
+        compareOutput(dstData, expectedData, expectedData.size());
+    }
 
-    // Read in inputs and expected outputs.
-    inputs[0] = readFromNpyFile<float>(input0_file.c_str(), input0_shape);
-    inputs[1] = readFromNpyFile<float>(input1_file.c_str(), input1_shape);
-    expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape);
+    TEST_CASE("op_entry_pad")
+    {
+        // Inputs/Outputs
+        tosa_datatype_t dt                = tosa_datatype_fp32_t;
+        std::vector<int32_t> input_shape  = { 2, 2 };
+        std::vector<int32_t> output_shape = { 4, 4 };
+        std::vector<float> srcData1(4, 4.0f);
+        std::vector<float> dstData(16, 0.0f);
+
+        tosa_tensor_t input1;
+        input1.shape     = input_shape.data();
+        input1.num_dims  = input_shape.size();
+        input1.data_type = dt;
+        input1.data      = reinterpret_cast<uint8_t*>(srcData1.data());
+        input1.size      = srcData1.size() * sizeof(float);
+
+        tosa_tensor_t output;
+        output.shape     = output_shape.data();
+        output.num_dims  = output_shape.size();
+        output.data_type = dt;
+        output.data      = reinterpret_cast<uint8_t*>(dstData.data());
+        output.size      = dstData.size() * sizeof(float);
+
+        // Execution
+        int32_t padding[4]    = { 1, 1, 1, 1 };
+        int32_t padding_len   = 4;
+        int32_t pad_const_int = 0;
+        float pad_const_fp    = 5.0f;
+        auto status           = tosa_run_pad(input1, padding_len, padding, pad_const_int, pad_const_fp, output);
+        CHECK((status == tosa_status_valid));
+
+        // Compare results
+        // Expect a 4x4 array with a border of 5's and inner 2x2 of 4's
+        std::vector<float> expectedData(16, 5.0f);
+        expectedData[5]  = 4.0f;
+        expectedData[6]  = 4.0f;
+        expectedData[9]  = 4.0f;
+        expectedData[10] = 4.0f;
+        compareOutput(dstData, expectedData, expectedData.size());
+    }
 
-    TosaSerializationHandler handler;
-    tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
-    CHECK((error == tosa::TOSA_OK));
+    TEST_CASE("simple_add_f32_test")
+    {
+        std::string test_root(std::string(PROJECT_ROOT) + "../examples/test_add_1x4x4x4_f32/");
+        std::string tosa_model_file(test_root + "flatbuffer-tflite/test_add_1x4x4x4_f32.tosa");
+        std::string input0_file(test_root + "placeholder_0.npy");
+        std::string input1_file(test_root + "placeholder_1.npy");
+        std::string expected_output_file(test_root + "tflite_result.npy");
 
-    GraphStatus status;
+        std::vector<std::string> input_names = { "TosaInput_0", "TosaInput_1" };
+        std::string output_name              = "TosaOutput_0";
 
-    // Initialize the ModelRunner with configurations.
-    IModelRunner runner;
-    status = runner.initialize(handler);
-    CHECK((status == GraphStatus::TOSA_VALID));
+        std::vector<int32_t> input0_shape = { 1, 4, 4, 1 };
+        std::vector<int32_t> input1_shape = { 1, 4, 4, 4 };
+        std::vector<int32_t> output_shape = { 1, 4, 4, 4 };
 
-    runner.setInput(input_names[0], inputs[0]);
-    runner.setInput(input_names[1], inputs[1]);
+        std::vector<std::vector<float>> inputs(input_names.size());
+        std::vector<float> actual_outputs   = {};
+        std::vector<float> expected_outputs = {};
 
-    // Run the ModelRunner using test inputs.
-    status = runner.run();
-    CHECK((status == GraphStatus::TOSA_VALID));
+        // Read in inputs and expected outputs.
+        inputs[0]        = readFromNpyFile<float>(input0_file.c_str(), input0_shape);
+        inputs[1]        = readFromNpyFile<float>(input1_file.c_str(), input1_shape);
+        expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape);
 
-    actual_outputs = runner.getOutput<float>(output_name);
-    CHECK(!actual_outputs.empty());
+        TosaSerializationHandler handler;
+        tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
+        CHECK((error == tosa::TOSA_OK));
 
-    compareOutput(expected_outputs, actual_outputs, expected_outputs.size());
-}
+        GraphStatus status;
 
-TEST_CASE("conv2d_f32_test")
-{
-    std::string test_root(std::string(PROJECT_ROOT) + "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/");
-    std::string tosa_model_file(test_root + "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa");
-    std::string input_file(test_root + "placeholder_0.npy");
-    std::string expected_output_file(test_root + "tflite_result.npy");
+        // Initialize the ModelRunner with configurations.
+        IModelRunner runner;
+        status = runner.initialize(handler);
+        CHECK((status == GraphStatus::TOSA_VALID));
 
-    std::string input_name = "TosaInput_0";
-    std::string output_name = "TosaOutput_0";
+        runner.setInput(input_names[0], inputs[0]);
+        runner.setInput(input_names[1], inputs[1]);
 
-    std::vector<int32_t> input_shape = { 1, 32, 32, 8 };
-    std::vector<int32_t> output_shape = { 1, 32, 32, 16 };
+        // Run the ModelRunner using test inputs.
+        status = runner.run();
+        CHECK((status == GraphStatus::TOSA_VALID));
 
-    // Read in inputs and expected outputs.
-    std::vector<float> inputs           = readFromNpyFile<float>(input_file.c_str(), input_shape);
-    std::vector<float> expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape);
+        actual_outputs = runner.getOutput<float>(output_name);
+        CHECK(!actual_outputs.empty());
 
-    TosaSerializationHandler handler;
-    tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
-    CHECK((error == tosa::TOSA_OK));
+        compareOutput(expected_outputs, actual_outputs, expected_outputs.size());
+    }
 
-    GraphStatus status;
+    TEST_CASE("conv2d_f32_test")
+    {
+        std::string test_root(std::string(PROJECT_ROOT) +
+                              "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/");
+        std::string tosa_model_file(test_root +
+                                    "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa");
+        std::string input_file(test_root + "placeholder_0.npy");
+        std::string expected_output_file(test_root + "tflite_result.npy");
 
-    // Initialize the ModelRunner with configurations.
-    IModelRunner runner;
-    status = runner.initialize(handler);
-    CHECK((status == GraphStatus::TOSA_VALID));
+        std::string input_name  = "TosaInput_0";
+        std::string output_name = "TosaOutput_0";
 
-    runner.setInput(input_name, inputs);
+        std::vector<int32_t> input_shape  = { 1, 32, 32, 8 };
+        std::vector<int32_t> output_shape = { 1, 32, 32, 16 };
 
-    // Run the ModelRunner using test inputs.
-    status = runner.run();
-    CHECK((status == GraphStatus::TOSA_VALID));
+        // Read in inputs and expected outputs.
+        std::vector<float> inputs           = readFromNpyFile<float>(input_file.c_str(), input_shape);
+        std::vector<float> expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape);
 
-    std::vector<float> actual_outputs = runner.getOutput<float>(output_name);
-    CHECK(!actual_outputs.empty());
+        TosaSerializationHandler handler;
+        tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
+        CHECK((error == tosa::TOSA_OK));
 
-    compareOutput(expected_outputs, actual_outputs, expected_outputs.size());
-}
+        GraphStatus status;
 
-TEST_CASE("conv2d_f32_validate_only_test")
-{
-    std::string test_root(std::string(PROJECT_ROOT) + "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/");
-    std::string tosa_model_file(test_root + "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa");
+        // Initialize the ModelRunner with configurations.
+        IModelRunner runner;
+        status = runner.initialize(handler);
+        CHECK((status == GraphStatus::TOSA_VALID));
 
-    TosaSerializationHandler handler;
-    tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
-    CHECK((error == tosa::TOSA_OK));
+        runner.setInput(input_name, inputs);
 
-    GraphStatus status;
-    func_debug_t funcDebug;
+        // Run the ModelRunner using test inputs.
+        status = runner.run();
+        CHECK((status == GraphStatus::TOSA_VALID));
 
-    func_config_t funcConfig;
-    funcConfig.validate_only = 1;
+        std::vector<float> actual_outputs = runner.getOutput<float>(output_name);
+        CHECK(!actual_outputs.empty());
 
-    // Initialize the ModelRunner with configurations.
-    IModelRunner runner = IModelRunner(funcConfig, funcDebug);
-    runner.setFuncConfig(funcConfig);
-    status = runner.initialize(handler);
-    CHECK((status == GraphStatus::TOSA_VALID));
+        compareOutput(expected_outputs, actual_outputs, expected_outputs.size());
+    }
 
-    // Run the ModelRunner using no inputs, as validate_only is specified run() should still work.
-    status = runner.run();
-    CHECK((status == GraphStatus::TOSA_VALID));
-}
+    TEST_CASE("conv2d_f32_validate_only_test")
+    {
+        std::string test_root(std::string(PROJECT_ROOT) +
+                              "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/");
+        std::string tosa_model_file(test_root +
+                                    "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa");
+
+        TosaSerializationHandler handler;
+        tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str());
+        CHECK((error == tosa::TOSA_OK));
+
+        GraphStatus status;
+        func_debug_t funcDebug;
+
+        func_config_t funcConfig;
+        funcConfig.validate_only = 1;
+
+        // Initialize the ModelRunner with configurations.
+        IModelRunner runner = IModelRunner(funcConfig, funcDebug);
+        runner.setFuncConfig(funcConfig);
+        status = runner.initialize(handler);
+        CHECK((status == GraphStatus::TOSA_VALID));
+
+        // Run the ModelRunner using no inputs, as validate_only is specified run() should still work.
+        status = runner.run();
+        CHECK((status == GraphStatus::TOSA_VALID));
+    }
 
-}
+}    // TEST_SUITE(model_runner)
diff --git a/scripts/operator_api/README.md b/scripts/operator_api/README.md
new file mode 100644
index 0000000..381d90c
--- /dev/null
+++ b/scripts/operator_api/README.md
@@ -0,0 +1,19 @@
+# Generate eager operator execution entrypoints
+
+## Introduction
+
+The generate_api.py script will generate an extended reference model API with eager operator execution entrypoints.
+The following files will be generated: include/operators.h and src/operators.cc
+
+## Requirements
+
+* Python 3.6 or later
+* Jinja2 (install with ```pip install Jinja2```)
+
+## Running from the command line
+
+The script can be run from the scripts/operator-api directory as follows:
+
+```bash
+python generate_api.py
+```
diff --git a/scripts/operator_api/generate_api.py b/scripts/operator_api/generate_api.py
new file mode 100644
index 0000000..1f89f74
--- /dev/null
+++ b/scripts/operator_api/generate_api.py
@@ -0,0 +1,349 @@
+"""Generate extended reference model API with eager operator execution entrypoints"""
+# Copyright (c) 2021-2022, ARM Limited.
+# SPDX-License-Identifier: Apache-2.0
+import copy
+import os
+import subprocess
+from xml.dom import minidom
+
+from jinja2 import Environment
+from jinja2 import FileSystemLoader
+
+
+def getTosaArgTypes(tosaXml):
+    """
+    Returns a list of the TOSA argument types from tosa.xml.
+    """
+    argTypes = {"in_t", "out_t", "mul_t", "weight_t", "in_out_t"}
+    argTypesXml = tosaXml.getElementsByTagName("type")
+    for argTypeXml in argTypesXml:
+        argTypes.add(argTypeXml.getAttribute("name"))
+    argTypes.remove("TABLE_SIZE")
+    return argTypes
+
+
+def getTosaDataTypes(tosaXml):
+    """
+    Returns a list of the TOSA data types from tosa.xml.
+    """
+    argTypes = getTosaArgTypes(tosaXml)
+    dataTypes = set()
+    dataTypesXml = tosaXml.getElementsByTagName("typesupport")
+    for dataTypeXml in dataTypesXml:
+        for argType in argTypes:
+            dataType = dataTypeXml.getAttribute(argType)
+            if dataType != "":
+                dataTypes.add(f"tosa_datatype_{dataType}")
+    return sorted(dataTypes)
+
+
+def getSerializeOpType(tosaOpName):
+    """
+    Returns the Serialization library operator that matches the TOSA operator specified.
+    """
+    map = {
+        "avg_pool2d": "Pool",
+        "conv2d": "Conv",
+        "conv3d": "Conv",
+        "depthwise_conv2d": "Conv",
+        "fully_connected": "FullyConnected",
+        "matmul": "MatMul",
+        "max_pool2d": "Pool",
+        "transpose_conv2d": "Conv",
+        "clamp": "Clamp",
+        "arithmetic_right_shift": "ArithmeticRightShift",
+        "mul": "Mul",
+        "table": "Table",
+        "negate": "Negate",
+        "pad": "Pad",
+        "reshape": "Reshape",
+        "slice": "Slice",
+        "tile": "Tile",
+        "transpose": "Transpose",
+        "resize": "Resize",
+        "rescale": "Rescale",
+        "cond_if": "CondIf",
+        "while_loop": "WhileLoop",
+    }
+    if tosaOpName not in map.keys():
+        return "None"
+    else:
+        return map[tosaOpName]
+
+
+def getSerializeArgsForOp(tosaOpName, allSerializeArgs, tosaArgs):
+    """
+    Returns the arguments required by the Serialization library for the TOSA operator specified.
+    Generates code to initialize Serialization arguments. If a matching TOSA argument exists,
+    that value is used for initialization, otherwise a default value e.g. 0 is used.
+    """
+    serOpType = getSerializeOpType(tosaOpName)
+    if serOpType not in allSerializeArgs.keys():
+        return {}
+    else:
+        serOpArgs = copy.deepcopy(allSerializeArgs[serOpType])
+        tosaArgsDict = {arg["name"]: arg for arg in tosaArgs}
+        serTosaTypeMap = {"ResizeMode": "tosa_mode"}
+        for arg in serOpArgs:
+            argName = arg["name"]
+            init = ""
+            # Translate TOSA data types to Serialization data types for initialization
+            if arg["dType"] in serTosaTypeMap.keys():
+                init = f" = translate_client_{serTosaTypeMap[arg['dType']]}(client_{argName})"
+            # Initialize Serialization arguments to their matching function parameter
+            elif argName in tosaArgsDict:
+                if arg["SV"] == "V":
+                    shape = tosaArgsDict[argName]["shape"]
+                    if shape == "[]":
+                        init = f"(&client_{argName}[0], &client_{argName}[0] + client_{argName}_len)"
+                    else:
+                        init = f"(&client_{argName}[0], &client_{argName}{shape})"
+                else:
+                    init = f" = client_{argName}"
+            else:
+                # Initialize Serialization arguments with no matching fuction parameter
+                if arg["SV"] == "V":
+                    init = ""
+                else:
+                    if arg["dType"] == "DType":
+                        arg["dType"] = "tosa::DType"
+                        init = " = tosa::DType::DType_FP32"
+                    else:
+                        init = " = 0"
+            arg["init"] = init
+        return serOpArgs
+
+
+def updateTosaArgs(tosaArgs, serializeArgs, tosaXml):
+    """
+    Replace TOSA argument data types with their matching Serialization argument data types.
+    Delete TOSA arguments where the type couldn't be determined.
+    Add Serialization arguments that have no matching TOSA argument.
+    """
+    tosaArgTypes = getTosaArgTypes(tosaXml)
+    serArgsDict = {arg["name"]: arg for arg in serializeArgs}
+    tosaArgsNames = [arg["name"] for arg in tosaArgs]
+    delTosaArgs = []
+    # Replace TOSA argument data types with their matching Serialization argument data types.
+    for tosaArg in tosaArgs:
+        if tosaArg["type"] in tosaArgTypes:
+            if tosaArg["name"] in serArgsDict:
+                tosaArg["type"] = serArgsDict[tosaArg["name"]]["dType"]
+            else:
+                # Delete TOSA argument whose data type can't be determined
+                delTosaArgs.append(tosaArgsNames.index(tosaArg["name"]))
+                # Delete corresponding length argument if one exists
+                lenArgName = f"{tosaArg['name']}_len"
+                if lenArgName in tosaArgsNames:
+                    delTosaArgs.append(tosaArgsNames.index(lenArgName))
+    # Delete TOSA arguments where the type couldn't be determined
+    for index in sorted(delTosaArgs, key=int, reverse=True):
+        del tosaArgs[index]
+    # Add Serialization arguments that have no matching TOSA argument
+    tosaArgNames = [arg["name"] for arg in tosaArgs]
+    for serArg in serializeArgs:
+        if (serArg["name"] not in tosaArgNames) and (
+            not serArg["dType"] == "tosa::DType"
+        ):
+            serArgName = serArg["name"]
+            if serArg["SV"] == "V":
+                # For vector data types, insert a matching length argument
+                tosaArgs.insert(
+                    len(tosaArgs) - 1,
+                    {
+                        "name": f"{serArgName}_len",
+                        "type": "int32_t",
+                        "shape": "",
+                        "category": "",
+                    },
+                )
+                init = f"(&client_{serArgName}[0], &client_{serArgName}[0] + client_{serArgName}_len)"
+                shape = "[]"
+            else:
+                init = f" = client_{serArg['name']}"
+                shape = ""
+            serArg["init"] = init
+            # Insert new argument
+            tosaArgs.insert(
+                len(tosaArgs) - 1,
+                {
+                    "name": serArgName,
+                    "type": serArg["dType"],
+                    "shape": shape,
+                    "category": "",
+                },
+            )
+
+
+def getOperators(tosaXml):
+    """
+    Return a list of TOSA operators as defined by tosa.xml.
+    """
+    operators = []
+    ignoreOps = ["while_loop", "cond_if", "const", "custom", "fft2d", "rfft2d"]
+    opsXml = tosaXml.getElementsByTagName("operator")
+    allSerializeArgs = getSerializeArgs()
+    for opXml in opsXml:
+        opName = opXml.getElementsByTagName("name")[0].firstChild.data.lower()
+        if opName not in ignoreOps:
+            operator = {"name": opName}
+            operator["serializeAttType"] = getSerializeOpType(opName)
+            tosaArgs = getTosaArgs(opXml)
+            serializeArgs = getSerializeArgsForOp(opName, allSerializeArgs, tosaArgs)
+            updateTosaArgs(tosaArgs, serializeArgs, tosaXml)
+            operator["arguments"] = tosaArgs
+            operator["serializeArgs"] = serializeArgs
+            operator["inputs"] = [
+                arg["name"] for arg in tosaArgs if arg["category"] == "input"
+            ]
+            operator["outputs"] = [
+                arg["name"] for arg in tosaArgs if arg["category"] == "output"
+            ]
+            operators.append(operator)
+    return operators
+
+
+def getTosaArgs(opXml):
+    """
+    Return the arguments required for the TOSA operator specified.
+    """
+    arguments = []
+    argsXml = opXml.getElementsByTagName("argument")
+    tosaTensorTypes = getTosaArgTypes(tosaXml)
+    tosaTypeMap = {"bool_t": "bool", "uint6_t": "uint8_t", "mode_t": "tosa_mode_t"}
+    for xmlArg in argsXml:
+        argName = xmlArg.getAttribute("name").lower()
+        argType = xmlArg.getAttribute("type")
+        argShape = xmlArg.getAttribute("shape")
+        argCategory = xmlArg.getAttribute("category")
+        # Update argument type
+        if argType[-1:] == "*":
+            argType = argType[:-1]
+        if argCategory in ["input", "output"] and argType in tosaTensorTypes:
+            argType = "tosa_tensor_t"
+            argShape = ""
+        if argType in tosaTypeMap:
+            argType = tosaTypeMap[argType]
+        # Add a length argument for arrays with unknown compile-time size
+        if argShape != "" and argShape[0] == "[" and not argShape[1:-1].isnumeric():
+            argShape = "[]"
+            arguments.append(
+                {
+                    "name": f"{argName}_len",
+                    "type": "int32_t",
+                    "shape": "",
+                    "category": "",
+                }
+            )
+        elif argShape == "" or not argShape[0] == "[":
+            argShape = ""
+        # Append argument
+        arguments.append(
+            {
+                "name": argName,
+                "type": argType,
+                "shape": argShape,
+                "category": argCategory,
+            }
+        )
+    return arguments
+
+
+def clangFormat(filename):
+    cmd = ["clang-format", "-i", filename]
+    with open(os.devnull, "w") as devnull:
+        subprocess.check_call(cmd, stdout=devnull)
+
+
+def getSerializeArgs():
+    """
+    Parse attribute.def file and return a dictionary where the keys are Serialization library operator names.
+    The values are the arguments required by each Serialization library operator.
+    """
+    serializeArgs = {}
+    with open("../../thirdparty/serialization_lib/include/attribute.def") as file:
+        preamble = True
+        inAtt = False
+        opName = ""
+        args = []
+        for line in file:
+            if preamble and not line[: len("DEF_ATTRIBUTE(")] == "DEF_ATTRIBUTE(":
+                continue
+            else:
+                preamble = False
+            line = line.lstrip().rstrip()
+            if not inAtt and "DEF_ATTRIBUTE(" in line:
+                opName = line[len("DEF_ATTRIBUTE(") : line.find(",")]
+                inAtt = True
+            elif inAtt:
+                vals = line.split(",")
+                argName = vals[2].lstrip().strip()
+                if ")" in argName:
+                    argName = argName[:-1]
+                arg = {
+                    "name": argName,
+                    "dType": vals[0].lstrip().strip(),
+                    "SV": vals[1].lstrip().strip(),
+                }
+                args.append(arg)
+                if ")" in line:
+                    serializeArgs[opName] = args
+                    opName = ""
+                    args = []
+                    inAtt = False
+    return serializeArgs
+
+
+def renderTemplate(environment, dataTypes, operators, template, outfile):
+    content = template.render(dataTypes=dataTypes, operators=operators)
+    with open(outfile, mode="w", encoding="utf-8") as output:
+        output.write(content)
+        print(f"Created {outfile}")
+
+    clangFormat(outfile)
+
+
+def generate(environment, dataTypes, operators):
+    # Generate include/operators.h
+    template = environment.get_template("operators_h.j2")
+    outfile = os.path.join("..", "..", "reference_model", "include", "operators.h")
+    renderTemplate(environment, dataTypes, operators, template, outfile)
+
+    # Generate src/operators.cc
+    template = environment.get_template("operators_cc.j2")
+    outfile = os.path.join("..", "..", "reference_model", "src", "operators.cc")
+    renderTemplate(environment, dataTypes, operators, template, outfile)
+
+
+def getSerializeOpTypeMap():
+    """
+    Utility function for generating the map used in getSerializeOpType()
+    """
+    import re
+
+    allSerializeArgs = getSerializeArgs()
+    serArgs = [
+        re.sub(r"(?<!^)(?=[A-Z])", "_", name).lower()
+        for name in allSerializeArgs.keys()
+    ]
+    serArgs = sorted(serArgs, key=len, reverse=True)
+    tosaXml = minidom.parse("../../thirdparty/specification/tosa.xml")
+    opsXml = tosaXml.getElementsByTagName("operator")
+    opNames = [
+        op.getElementsByTagName("name")[0].firstChild.data.lower() for op in opsXml
+    ]
+    map = {}
+    for opName in opNames:
+        for serArg in serArgs:
+            if serArg in opName:
+                components = serArg.split("_")
+                map[opName] = "".join(x.title() for x in components)
+    return map
+
+
+if __name__ == "__main__":
+    environment = Environment(loader=FileSystemLoader("templates/"))
+    tosaXml = minidom.parse("../../thirdparty/specification/tosa.xml")
+    dataTypes = getTosaDataTypes(tosaXml)
+    operators = getOperators(tosaXml)
+    generate(environment, dataTypes, operators)
diff --git a/scripts/operator_api/templates/operators_cc.j2 b/scripts/operator_api/templates/operators_cc.j2
new file mode 100644
index 0000000..6b0ed6e
--- /dev/null
+++ b/scripts/operator_api/templates/operators_cc.j2
@@ -0,0 +1,176 @@
+
+// Copyright (c) 2022, ARM Limited.
+//
+//    Licensed under the Apache License, Version 2.0 (the "License");
+//    you may not use this file except in compliance with the License.
+//    You may obtain a copy of the License at
+//
+//         http://www.apache.org/licenses/LICENSE-2.0
+//
+//    Unless required by applicable law or agreed to in writing, software
+//    distributed under the License is distributed on an "AS IS" BASIS,
+//    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//    See the License for the specific language governing permissions and
+//    limitations under the License.
+
+// THIS FILE IS GENERATED. DO NOT EDIT!
+// See scripts/operator_api/generate_api.py
+
+#include "operators.h"
+#include "model_runner_impl.h"
+#include "ops/op_factory.h"
+
+#define TOSA_RETURN_ON_ERROR(status)                                                                                   \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (status != 0)                                                                                               \
+        {                                                                                                              \
+            return tosa_status_error;                                                                                  \
+        }                                                                                                              \
+    } while (false)
+
+#define TOSA_RETURN_ON_GRAPH_STATUS_ERROR(status)                                                                      \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (status != GraphStatus::TOSA_VALID)                                                                         \
+        {                                                                                                              \
+            auto ustatus = static_cast<std::underlying_type_t<GraphStatus>>(status);                                   \
+            return static_cast<tosa_status_t>(ustatus);                                                                \
+        }                                                                                                              \
+    } while (false)
+
+namespace {
+
+tosa::DType translate_client_datatype(tosa_datatype_t type)
+{
+    switch (type)
+    {
+        case tosa_datatype_fp16_t:
+            return tosa::DType::DType_FP16;
+        case tosa_datatype_fp32_t:
+            return tosa::DType::DType_FP32;
+        default:
+            return tosa::DType::DType_UNKNOWN;
+    }
+};
+
+tosa::TosaSerializationTensor* translate_client_tensor(tosa_tensor_t& tensor, const std::string& name)
+{
+    std::vector<int32_t> shape(tensor.shape, tensor.shape + tensor.num_dims);
+    return new tosa::TosaSerializationTensor(name, shape, translate_client_datatype(tensor.data_type), {});
+}
+
+tosa::ResizeMode translate_client_tosa_mode(tosa_mode_t mode) {
+    switch(mode) {
+        case tosa_mode_nearest:
+            return tosa::ResizeMode_NEAREST;
+        case tosa_mode_max:            
+        case tosa_mode_bilinear:
+            return tosa::ResizeMode_BILINEAR;
+        default:
+            return tosa::ResizeMode_UNKNOWN;            
+    }
+}
+
+}    // namespace
+
+extern "C"
+{
+    {% for operator in operators: %}
+    tosa_status_t tosa_run_{{ operator.name }} (
+        {%- for arg in operator.arguments: -%}
+            {% if arg.type != "tosa_tensor_t" -%}const {% endif -%}{{arg.type}} client_{{arg.name}}{{arg.shape}}
+            {% if loop.index < operator.arguments|length %},{% endif %}
+        {%- endfor -%}
+    )
+    {
+        // Create operator attributes
+        {% for arg in operator.serializeArgs: %}
+            {%- if arg.SV == "V": -%}
+                const std::vector<{{arg.dType}}> {{arg.name}}{{arg.init}};
+            {%- else: -%}
+                const {{arg.dType}} {{arg.name}}{{arg.init}};
+            {%- endif -%}
+        {%- endfor -%}
+
+        Tosa{{operator.serializeAttType}}Attribute attr
+        {%- if operator.serializeArgs|length > 0 -%}
+        (
+            {%- for arg in operator.serializeArgs: -%}
+                {{arg.name}}{% if loop.index < operator.serializeArgs|length %}, {% endif %}
+            {%- endfor -%}
+        )
+        {%- endif -%};
+
+        // Create tensors
+        {% for input in operator.inputs: -%}
+            tosa::TosaSerializationTensor* {{input}}  = translate_client_tensor(client_{{input}}, "{{input}}");
+        {%- endfor -%}
+        {% for output in operator.outputs: %}
+            tosa::TosaSerializationTensor* {{output}}  = translate_client_tensor(client_{{output}}, "{{output}}");
+        {%- endfor %}
+
+        // Create operator
+        auto op = new tosa::TosaSerializationOperator(tosa::Op::Op_{{operator.name|upper}},
+                                                      {%- if operator.serializeAttType != "None" -%}
+                                                        tosa::Attribute::Attribute_{{operator.serializeAttType}}Attribute
+                                                      {%- else -%}
+                                                        tosa::Attribute::Attribute_NONE
+                                                      {%- endif -%},
+                                                      &attr, {
+                                                                {%- for input in operator.inputs: -%}
+                                                                    {{input}}->GetName()
+                                                                    {%- if loop.index < operator.inputs|length -%},{%- endif -%}
+                                                                {%- endfor -%}
+                                                             },
+                                                             {
+                                                                {%- for output in operator.outputs: -%}
+                                                                    {{output}}->GetName()
+                                                                    {%- if loop.index < operator.outputs|length -%},{%- endif -%}
+                                                                {%- endfor -%}
+                                                             });
+
+        // Create a tosa single-op basic block
+        tosa::TosaSerializationBasicBlock block("{{operator.name}}", { op },
+                                                 {
+                                                    {%- for input in operator.inputs: -%}
+                                                        {{input}},
+                                                    {%- endfor -%}
+                                                    {%- for output in operator.outputs: -%}
+                                                        {{output}}
+                                                        {%- if loop.index < operator.outputs|length -%},{%- endif -%}
+                                                    {%- endfor -%}
+                                                 },
+                                                 {
+                                                    {%- for input in operator.inputs: -%}
+                                                        {{input}}->GetName()
+                                                        {%- if loop.index < operator.inputs|length -%},{%- endif -%}
+                                                    {%- endfor -%}
+                                                 },
+                                                 {
+                                                    {%- for output in operator.outputs: -%}
+                                                        {{output}}->GetName()
+                                                        {%- if loop.index < operator.outputs|length -%},{%- endif -%}
+                                                    {%- endfor -%}
+                                                 });
+
+        // Setup model
+        TosaReference::ModelRunnerImpl runner;
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.initialize(block));
+        {% for input in operator.inputs: -%}
+            TOSA_RETURN_ON_ERROR(runner.setInput({{input}}->GetName(), client_{{input}}.data, client_{{input}}.size));
+        {%- endfor %}
+
+        // Execute
+        TOSA_RETURN_ON_GRAPH_STATUS_ERROR(runner.run());
+
+        // Extract outputs
+        {% for output in operator.outputs: -%}
+            TOSA_RETURN_ON_ERROR(runner.getOutput({{output}}->GetName(), client_{{output}}.data, client_{{output}}.size));
+        {%- endfor %}
+
+        return tosa_status_valid;
+    }
+    {% endfor %}
+
+}    // extern "C"
\ No newline at end of file
diff --git a/scripts/operator_api/templates/operators_h.j2 b/scripts/operator_api/templates/operators_h.j2
new file mode 100644
index 0000000..803b76a
--- /dev/null
+++ b/scripts/operator_api/templates/operators_h.j2
@@ -0,0 +1,74 @@
+
+// Copyright (c) 2022, ARM Limited.
+//
+//    Licensed under the Apache License, Version 2.0 (the "License");
+//    you may not use this file except in compliance with the License.
+//    You may obtain a copy of the License at
+//
+//         http://www.apache.org/licenses/LICENSE-2.0
+//
+//    Unless required by applicable law or agreed to in writing, software
+//    distributed under the License is distributed on an "AS IS" BASIS,
+//    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//    See the License for the specific language governing permissions and
+//    limitations under the License.
+
+// THIS FILE IS GENERATED. DO NOT EDIT!
+// See scripts/operator_api/generate_api.py
+
+#ifndef OPERATORS_H_
+#define OPERATORS_H_
+
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+    // Note status needs to be aligned with graph_status
+    enum tosa_status_t
+    {
+        tosa_status_valid         = 0,
+        tosa_status_unpredictable = 1,
+        tosa_status_error         = 2
+    };
+
+    enum tosa_mode_t
+    {
+        tosa_mode_unknown  = 0,
+        tosa_mode_nearest  = 1,
+        tosa_mode_bilinear = 2,
+        tosa_mode_min      = 3,
+        tosa_mode_max      = 4
+    };
+
+    enum tosa_datatype_t
+    {
+        {% for dataType in dataTypes: -%}
+            {{dataType}} = {{loop.index-1}},
+        {% endfor -%}
+    };
+
+    struct tosa_tensor_t
+    {
+        int32_t* shape;
+        int32_t num_dims;
+        tosa_datatype_t data_type;
+        uint8_t* data;
+        size_t size;
+    };
+
+    {% for operator in operators: %}
+        tosa_status_t tosa_run_{{ operator.name }} (
+            {%- for arg in operator.arguments: -%}
+                {% if arg.type != "tosa_tensor_t" -%}const {% endif -%}{{arg.type}} client_{{arg.name}}{{arg.shape}}
+                {% if loop.index < operator.arguments|length %},{% endif %}
+            {%- endfor -%});
+    {% endfor %}
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif // OPERATORS_H_
\ No newline at end of file
diff --git a/thirdparty/specification b/thirdparty/specification
new file mode 160000
+Subproject 0205d99cbff58797bf6602ee5718d50c00d8309