Optimize NEStackLayer

Optimize the stack operation in Cpu by leveraging block memcpy.

Resolves: COMPMID-6498

Change-Id: I49d79d179f0375a73d654edd59fb33072112569b
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10451
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 10 insertions, 21 deletions

diff --git a/src/runtime/NEON/functions/NEStackLayer.cpp b/src/runtime/NEON/functions/NEStackLayer.cpp
index 03e7026691..2f88ffca2a 100644
--- a/src/runtime/NEON/functions/NEStackLayer.cpp
+++ b/src/runtime/NEON/functions/NEStackLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2021 Arm Limited.
+ * Copyright (c) 2018-2021, 2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -39,7 +39,7 @@ namespace arm_compute
 NEStackLayer::~NEStackLayer() = default;
 
 NEStackLayer::NEStackLayer() // NOLINT
-    : _input(), _stack_kernels(), _num_inputs(0)
+    : _stack_kernel(std::make_unique<NEStackLayerKernel>()), _is_prepared(false)
 {
 }
 
@@ -47,17 +47,10 @@ void NEStackLayer::configure(const std::vector<ITensor *> &input, int axis, ITen
 {
     ARM_COMPUTE_LOG_PARAMS(input, axis, output);
 
-    _num_inputs = input.size();
-    _stack_kernels.resize(_num_inputs);
-
     // Wrap around negative values
     const unsigned int axis_u = wrap_around(axis, static_cast<int>(input[0]->info()->num_dimensions() + 1));
 
-    for (unsigned int i = 0; i < _num_inputs; i++)
-    {
-        _stack_kernels[i] = std::make_unique<NEStackLayerKernel>();
-        _stack_kernels[i]->configure(input[i], axis_u, i, _num_inputs, output);
-    }
+    _stack_kernel->configure(input, axis_u, output);
 }
 
 Status NEStackLayer::validate(const std::vector<ITensorInfo *> &input, int axis, const ITensorInfo *output)
@@ -69,24 +62,20 @@ Status NEStackLayer::validate(const std::vector<ITensorInfo *> &input, int axis,
     const size_t       rank   = input[0]->num_dimensions();
     const unsigned int axis_u = wrap_around(axis, static_cast<int>(rank + 1));
 
-    const unsigned int num_inputs = input.size();
-
-    for (unsigned int i = 0; i < num_inputs; i++)
-    {
-        // All the tensors must have the same rank
-        ARM_COMPUTE_RETURN_ERROR_ON(input[i]->num_dimensions() != rank);
-        // Validate Kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(NEStackLayerKernel::validate(input[i], axis_u, i, num_inputs, output));
-    }
+    // Validate Kernel
+    ARM_COMPUTE_RETURN_ON_ERROR(NEStackLayerKernel::validate(input, axis_u, output));
 
     return Status{};
 }
 
 void NEStackLayer::run()
 {
-    for (unsigned i = 0; i < _num_inputs; i++)
+    if (!_is_prepared)
     {
-        NEScheduler::get().schedule(_stack_kernels[i].get(), Window::DimY);
+        _stack_kernel->prepare();
+        _is_prepared = true;
     }
+
+    NEScheduler::get().schedule(_stack_kernel.get(), _stack_kernel->get_split_dimension());
 }
 } // namespace arm_compute