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diff --git a/tests/validation/CL/UNIT/Multithreaded.cpp b/tests/validation/CL/UNIT/Multithreaded.cpp
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+++ b/tests/validation/CL/UNIT/Multithreaded.cpp
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+/*
+ * Copyright (c) 2022 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/RuntimeContext.h"
+
+#include "tests/CL/CLAccessor.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/ParametersLibrary.h"
+#include "tests/validation/Validation.h"
+#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
+#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
+#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/PixelWiseMultiplication.h"
+#include <thread>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+TEST_SUITE(CL)
+TEST_SUITE(UNIT)
+TEST_SUITE(RuntimeContext)
+// This test tries scheduling work concurrently from two independent threads
+TEST_CASE(MultipleThreadedScheduller, framework::DatasetMode::ALL)
+{
+    constexpr auto num_threads(16u);
+    std::array<CLActivationLayer, num_threads>         func{};
+    std::array<CLPixelWiseMultiplication, num_threads> pmul{};
+    std::array<CLTensor, num_threads>                  s0{};
+    std::array<CLTensor, num_threads>                  s1{};
+
+    std::array<CLTensor, num_threads> st{};
+    std::array<CLTensor, num_threads> dt{};
+
+    const TensorShape         tensor_shape(128u, 4u, 5u);
+    const ActivationLayerInfo ainfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.5f, 1.f);
+    std::array<std::thread, num_threads> threads;
+    auto ctx = parameters->get_ctx<CLTensor>();
+
+    for(auto i = 0u; i < num_threads; ++i)
+    {
+        s0[i]   = create_tensor<CLTensor>(tensor_shape, DataType::F32, 1);
+        s1[i]   = create_tensor<CLTensor>(tensor_shape, DataType::F32, 1);
+        st[i]   = create_tensor<CLTensor>(tensor_shape, DataType::F32, 1);
+        dt[i]   = create_tensor<CLTensor>(tensor_shape, DataType::F32, 1);
+        func[i] = CLActivationLayer(ctx);
+        pmul[i] = CLPixelWiseMultiplication();
+        threads[i] =
+            std::thread([&,i]
+        {
+            auto &s  = st[i];
+            auto &t  = dt[i];
+            auto &p0 = s0[i];
+            auto &p1 = s1[i];
+            pmul[i].configure(&p0, &p1, &s, 1.f, ConvertPolicy::WRAP, RoundingPolicy::TO_NEAREST_UP);
+            func[i].configure(&s, &t, ainfo);
+            s.allocator()->allocate();
+            t.allocator()->allocate();
+            p0.allocator()->allocate();
+            p1.allocator()->allocate();
+            library->fill_tensor_uniform(CLAccessor(p0), 0, -1.f, 1.f);
+            library->fill_tensor_uniform(CLAccessor(p1), 0, -1.f, 1.f);
+            pmul[i].run();
+            func[i].run();
+        });
+    }
+
+    for(auto &t : threads)
+    {
+        t.join();
+    }
+
+    SimpleTensor<float> rs{ tensor_shape, DataType::F32, 1 };
+    SimpleTensor<float> ra{ tensor_shape, DataType::F32, 1 };
+    SimpleTensor<float> rb{ tensor_shape, DataType::F32, 1 };
+    library->fill_tensor_uniform(ra, 0, -1.f, 1.f);
+    library->fill_tensor_uniform(rb, 0, -1.f, 1.f);
+    const auto mul    = reference::pixel_wise_multiplication<float, float, float>(ra, rb, 1.f, ConvertPolicy::WRAP, RoundingPolicy::TO_NEAREST_UP, DataType::F32);
+    const auto golden = reference::activation_layer<float>(mul, ainfo);
+    for(auto &d : dt)
+    {
+        validate(CLAccessor(d), golden);
+    }
+}
+
+TEST_SUITE_END() // MultipleThreadedScheduller
+TEST_SUITE_END() // UNIT
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute