diff --git a/onert-micro/eval-driver/Driver.cpp b/onert-micro/eval-driver/Driver.cpp
index b4570e5776e..9049e9eeb56 100644
--- a/onert-micro/eval-driver/Driver.cpp
+++ b/onert-micro/eval-driver/Driver.cpp
@@ -114,7 +114,7 @@ int entry(int argc, char **argv)
     }
 
     // Do inference.
-    interpreter.run();
+    interpreter.run(config);
   }
 
   // Get output.
diff --git a/onert-micro/onert-micro/include/OMInterpreter.h b/onert-micro/onert-micro/include/OMInterpreter.h
index d450c3403e4..cb63cc1bf20 100644
--- a/onert-micro/onert-micro/include/OMInterpreter.h
+++ b/onert-micro/onert-micro/include/OMInterpreter.h
@@ -40,7 +40,7 @@ class OMInterpreter
 
   OMStatus importModel(const char *model_ptr, const OMConfig &config);
 
-  OMStatus run();
+  OMStatus run(const OMConfig &config);
 
   OMStatus reset();
 
diff --git a/onert-micro/onert-micro/include/OMTrainingInterpreter.h b/onert-micro/onert-micro/include/OMTrainingInterpreter.h
index b3dbcd8987b..815908ef70b 100644
--- a/onert-micro/onert-micro/include/OMTrainingInterpreter.h
+++ b/onert-micro/onert-micro/include/OMTrainingInterpreter.h
@@ -68,9 +68,10 @@ class OMTrainingInterpreter
   // Note: calculation will be done on test_size number of test samples
   // Warning: before using evaluateMetric call: 1) importTrainModel; 2) setInput; 3) setTarget
   // Note: number of the samples in data should be equal to the test_size
-  OMStatus evaluateMetric(OMMetrics metric, void *metric_val, uint32_t test_size)
+  OMStatus evaluateMetric(const OMConfig &config, OMMetrics metric, void *metric_val,
+                          uint32_t test_size)
   {
-    return _training_runtime_module.evaluateMetric(metric, metric_val, test_size);
+    return _training_runtime_module.evaluateMetric(config, metric, metric_val, test_size);
   }
 
   // To get input and output flat size
@@ -86,7 +87,7 @@ class OMTrainingInterpreter
   // Load current status from checkpoint and save it in current model and in current config
   OMStatus loadCheckpoint(OMConfig &config, const char *load_path);
 
-  OMStatus run() { return _training_runtime_module.run(); }
+  OMStatus run(const OMConfig &config) { return _training_runtime_module.run(config); }
   OMStatus allocateInputs() { return _training_runtime_module.allocateInputs(); }
 
   void *getInputData(uint32_t position);
diff --git a/onert-micro/onert-micro/include/core/OMRuntimeModule.h b/onert-micro/onert-micro/include/core/OMRuntimeModule.h
index 53ba519a1ef..c5e368d7238 100644
--- a/onert-micro/onert-micro/include/core/OMRuntimeModule.h
+++ b/onert-micro/onert-micro/include/core/OMRuntimeModule.h
@@ -43,7 +43,7 @@ class OMRuntimeModule
   ~OMRuntimeModule() = default;
 
   OMStatus importModel(const char *model_ptr, const OMConfig &config);
-  OMStatus run();
+  OMStatus run(const OMConfig &config);
   OMStatus reset();
 
   uint32_t getNumberOfInputs();
diff --git a/onert-micro/onert-micro/include/core/OMTrainingRuntimeModule.h b/onert-micro/onert-micro/include/core/OMTrainingRuntimeModule.h
index 9b53c3bd888..c3b00ce5450 100644
--- a/onert-micro/onert-micro/include/core/OMTrainingRuntimeModule.h
+++ b/onert-micro/onert-micro/include/core/OMTrainingRuntimeModule.h
@@ -68,7 +68,8 @@ class OMTrainingRuntimeModule : public OMRuntimeModule
   //          2) metric_val should be initialized with some value before calling this method due to
   //             after calculation for current batch_num (the sequence number of the current sample)
   //             this value is added to metric_val
-  OMStatus evaluateMetric(OMMetrics metric, void *metric_val, uint32_t test_size);
+  OMStatus evaluateMetric(const OMConfig &config, OMMetrics metric, void *metric_val,
+                          uint32_t test_size);
 
   // Set input data for input with input_index
   // Note: number of the samples in data should be equal to the batch_size in config structure
diff --git a/onert-micro/onert-micro/include/execute/OMExecuteArgs.h b/onert-micro/onert-micro/include/execute/OMExecuteArgs.h
index 196f68a87a3..feb1318e5b7 100644
--- a/onert-micro/onert-micro/include/execute/OMExecuteArgs.h
+++ b/onert-micro/onert-micro/include/execute/OMExecuteArgs.h
@@ -33,6 +33,8 @@ struct OMExecuteArgs
   core::OMRuntimeContext &runtime_context;
   uint16_t kernel_index;
   core::OMRuntimeModule &runtime_module;
+  uint32_t num_train_layers = 0;
+  bool is_train_mode = false;
 };
 
 } // namespace execute
diff --git a/onert-micro/onert-micro/include/execute/OMRuntimeKernel.h b/onert-micro/onert-micro/include/execute/OMRuntimeKernel.h
index b6f63cdaa48..e33239f7256 100644
--- a/onert-micro/onert-micro/include/execute/OMRuntimeKernel.h
+++ b/onert-micro/onert-micro/include/execute/OMRuntimeKernel.h
@@ -23,7 +23,7 @@
 
 #include <cstdint>
 
-constexpr static uint32_t maxInputSize = 5;
+constexpr static uint32_t maxInputSize = 6;
 constexpr static uint32_t maxOutputSize = 5;
 
 namespace onert_micro
diff --git a/onert-micro/onert-micro/include/execute/OMTestUtils.h b/onert-micro/onert-micro/include/execute/OMTestUtils.h
index eaef8c0d457..e5d685db72a 100644
--- a/onert-micro/onert-micro/include/execute/OMTestUtils.h
+++ b/onert-micro/onert-micro/include/execute/OMTestUtils.h
@@ -61,7 +61,7 @@ std::vector<U> checkKernel(uint32_t num_inputs,
     }
   }
 
-  interpreter.run();
+  interpreter.run(config);
 
   U *output_data = reinterpret_cast<U *>(interpreter.getOutputDataAt(0));
   const size_t num_elements = interpreter.getOutputSizeAt(0);
diff --git a/onert-micro/onert-micro/include/pal/common/PALGRUCommon.h b/onert-micro/onert-micro/include/pal/common/PALGRUCommon.h
new file mode 100644
index 00000000000..945122c32d7
--- /dev/null
+++ b/onert-micro/onert-micro/include/pal/common/PALGRUCommon.h
@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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 ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H
+#define ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H
+
+#include "OMStatus.h"
+#include "core/OMRuntimeShape.h"
+
+#include "PALUtils.h"
+#include "ProcessBroadcastShapes.h"
+#include "PALFullyConnected.h"
+#include "PALLogistic.h"
+
+namespace onert_micro
+{
+namespace execute
+{
+namespace pal
+{
+namespace
+{
+void calculateGRU(const float *input_data, const float *weight_input_data,
+                  const float *weight_hidden_data, const float *bias_input_data,
+                  const float *bias_hidden_data, float *output_data,
+                  const core::OMRuntimeShape &input_shape, const core::OMRuntimeShape &output_shape,
+                  const core::OMRuntimeShape &weight_input_shape,
+                  const core::OMRuntimeShape &weight_hidden_shape, float *output_input_data,
+                  float *output_hidden_data, const core::OMRuntimeShape &output_shape_fc,
+                  float *intermediate_buffer)
+{
+  core::FullyConnectedParams op_params{};
+  // As FC nodes doesn't have any activations inside GRU, let' use just numeric limits
+  op_params.float_activation_min = std::numeric_limits<float>::lowest();
+  op_params.float_activation_max = std::numeric_limits<float>::max();
+  // If intermediate_buffer != nullptr - then it is train mode and we need save intermediate inform
+  bool is_train_mode = intermediate_buffer != nullptr;
+  if (is_train_mode)
+  {
+    // Copy input for FC Input to calculate weights gradients
+    std::memcpy(intermediate_buffer, output_data, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  // FC Input
+  FullyConnected(op_params, output_data, weight_input_shape, weight_input_data, bias_input_data,
+                 output_shape_fc, output_input_data);
+
+  // FC Hidden
+  // Note: input for this FC node will be saved without intermediate buffer
+  FullyConnected(op_params, input_data, weight_hidden_shape, weight_hidden_data, bias_hidden_data,
+                 output_shape_fc, output_hidden_data);
+
+  int num_elements = output_shape_fc.dims(1) / 3;
+
+  float *second_hidden_part = output_hidden_data + num_elements;
+  float *second_input_part = output_input_data + num_elements;
+
+  float *third_hidden_part = second_hidden_part + num_elements;
+  float *third_input_part = second_input_part + num_elements;
+
+  // Calculate Left part
+  for (int i = 0; i < num_elements; ++i)
+  {
+    output_input_data[i] += output_hidden_data[i];
+  }
+
+  // If train mode - save logistic input
+  if (is_train_mode)
+  {
+    std::memcpy(intermediate_buffer, output_input_data, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  Logistic(num_elements, output_input_data, output_input_data);
+
+  // If train mode - save most left mul input (right input)
+  if (is_train_mode)
+  {
+    std::memcpy(intermediate_buffer, output_input_data, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  // Calculate most left mul
+  float *most_left_part_final = output_input_data;
+  float *first_part = output_input_data;
+  for (int i = 0; i < num_elements; ++i)
+  {
+    output_data[i] *= most_left_part_final[i];
+    first_part[i] = 1.0f - first_part[i];
+  }
+
+  // Calc second part
+  for (int i = 0; i < num_elements; ++i)
+  {
+    second_hidden_part[i] += second_input_part[i];
+  }
+  // If train mode - save logistic input
+  if (is_train_mode)
+  {
+    std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  Logistic(num_elements, second_hidden_part, second_hidden_part);
+
+  // If train mode - save mul input (left and right)
+  if (is_train_mode)
+  {
+    // Left input
+    std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+
+    // Right input
+    std::memcpy(intermediate_buffer, third_input_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  for (int i = 0; i < num_elements; ++i)
+  {
+    second_hidden_part[i] *= third_input_part[i];
+    second_hidden_part[i] += third_hidden_part[i];
+  }
+  // If train mode - save tanh input
+  if (is_train_mode)
+  {
+    std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  for (int i = 0; i < num_elements; ++i)
+  {
+    second_hidden_part[i] = std::tanh(second_hidden_part[i]);
+  }
+
+  // If train mode - save mul input (left and right)
+  if (is_train_mode)
+  {
+    // Left input
+    std::memcpy(intermediate_buffer, first_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+
+    // Right input
+    std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float));
+    // Move intermediate_buffer pointer
+    intermediate_buffer += output_shape.flatSize();
+  }
+  for (int i = 0; i < num_elements; ++i)
+  {
+    second_hidden_part[i] *= first_part[i];
+    output_data[i] += second_hidden_part[i];
+  }
+}
+
+} // namespace
+
+OMStatus GRU(const float *input_data, const float *weight_input_data,
+             const float *weight_hidden_data, const float *bias_input_data,
+             const float *bias_hidden_data, const float *hidden_state_data, float *output_data,
+             float *output_input_data, float *output_hidden_data,
+             const core::OMRuntimeShape &input_shape, const core::OMRuntimeShape &output_shape,
+             const core::OMRuntimeShape &weight_input_shape,
+             const core::OMRuntimeShape &weight_hidden_shape, const size_t intermediate_buffer_size,
+             float *intermediate_buffer)
+{
+  const int32_t time = input_shape.dims(0);
+
+  core::OMRuntimeShape output_shape_fc(2);
+  output_shape_fc.setDim(0, 1);
+  output_shape_fc.setDim(1, weight_hidden_shape.dims(0));
+
+  std::memcpy(output_data, hidden_state_data, output_shape.flatSize() * sizeof(float));
+
+  for (int i = 0; i < time; ++i)
+  {
+    calculateGRU(input_data, weight_input_data, weight_hidden_data, bias_input_data,
+                 bias_hidden_data, output_data, input_shape, output_shape, weight_input_shape,
+                 weight_hidden_shape, output_input_data, output_hidden_data, output_shape_fc,
+                 intermediate_buffer);
+    input_data += input_shape.dims(2);
+    if (intermediate_buffer_size != 0)
+    {
+      assert(intermediate_buffer != nullptr);
+      intermediate_buffer += intermediate_buffer_size;
+    }
+  }
+  return Ok;
+}
+
+} // namespace pal
+} // namespace execute
+} // namespace onert_micro
+
+#endif // ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H
diff --git a/onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst b/onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst
index 2560554777a..836c2a246c8 100644
--- a/onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst
+++ b/onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst
@@ -23,6 +23,7 @@ REGISTER_KERNEL(GATHER_ND, GatherND)
 REGISTER_KERNEL(EXP, Exp)
 REGISTER_KERNEL(GREATER, Greater)
 REGISTER_KERNEL(GREATER_EQUAL, GreaterEqual)
+REGISTER_KERNEL(GRU, GRU)
 REGISTER_KERNEL(EXPAND_DIMS, ExpandDims)
 REGISTER_KERNEL(ELU, Elu)
 REGISTER_KERNEL(EQUAL, Equal)
diff --git a/onert-micro/onert-micro/include/pal/mcu/PALGRU.h b/onert-micro/onert-micro/include/pal/mcu/PALGRU.h
new file mode 100644
index 00000000000..75389fe5f7e
--- /dev/null
+++ b/onert-micro/onert-micro/include/pal/mcu/PALGRU.h
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+ *
+ * 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 ONERT_MICRO_EXECUTE_PAL_GRU_H
+#define ONERT_MICRO_EXECUTE_PAL_GRU_H
+
+#include "PALGRUCommon.h"
+
+#endif // ONERT_MICRO_EXECUTE_PAL_GRU_H
diff --git a/onert-micro/onert-micro/include/test_models/gru/FloatGRUKernel.h b/onert-micro/onert-micro/include/test_models/gru/FloatGRUKernel.h
new file mode 100644
index 00000000000..fa49b29c1d0
--- /dev/null
+++ b/onert-micro/onert-micro/include/test_models/gru/FloatGRUKernel.h
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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 ONERT_MICRO_TEST_MODELS_FLOAT_GRU_KERNEL_H
+#define ONERT_MICRO_TEST_MODELS_FLOAT_GRU_KERNEL_H
+
+#include "TestDataGRUBase.h"
+
+namespace onert_micro
+{
+namespace test_model
+{
+
+namespace gru_float
+{
+/*
+ * GRU Kernel:
+ *
+ *        Input(1, 1, 6)
+ *            |
+ *           GRU
+ *            |
+ *        Output(1, 1, 5)
+ */
+unsigned char test_kernel_model_circle[] = {
+  0x1c, 0x00, 0x00, 0x00, 0x43, 0x49, 0x52, 0x30, 0x00, 0x00, 0x12, 0x00, 0x18, 0x00, 0x00, 0x00,
+  0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x12, 0x00, 0x00, 0x00,
+  0x54, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+  0x30, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0xb4, 0x06, 0x00, 0x00, 0x98, 0x06, 0x00, 0x00,
+  0xe0, 0x04, 0x00, 0x00, 0x70, 0x03, 0x00, 0x00, 0x1c, 0x03, 0x00, 0x00, 0xd8, 0x02, 0x00, 0x00,
+  0x90, 0x02, 0x00, 0x00, 0x38, 0x02, 0x00, 0x00, 0xe8, 0x01, 0x00, 0x00, 0xa8, 0x01, 0x00, 0x00,
+  0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x6e, 0x6e, 0x70, 0x61, 0x63, 0x6b, 0x61, 0x67,
+  0x65, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
+  0xf4, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0xfb, 0xfb, 0xff, 0xff, 0xff, 0x0c, 0x00, 0x0c, 0x00,
+  0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2d,
+  0x2d, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00,
+  0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00,
+  0x3c, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
+  0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00,
+  0x02, 0x00, 0x00, 0x00, 0xb4, 0x00, 0x00, 0x00, 0x4c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
+  0xac, 0x05, 0x00, 0x00, 0xf4, 0x03, 0x00, 0x00, 0x88, 0x02, 0x00, 0x00, 0x2c, 0x02, 0x00, 0x00,
+  0xf0, 0x01, 0x00, 0x00, 0xa4, 0x01, 0x00, 0x00, 0x48, 0x01, 0x00, 0x00, 0xf8, 0x00, 0x00, 0x00,
+  0xb4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x1a, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00,
+  0x07, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x00,
+  0x34, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00,
+  0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x0e, 0x00, 0x00, 0x00,
+  0x06, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
+  0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x00, 0x00,
+  0x08, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xfb,
+  0x18, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xc4, 0xfa, 0xff, 0xff,
+  0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+  0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
+  0x03, 0x00, 0x00, 0x00, 0x34, 0xfb, 0xff, 0xff, 0x30, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
+  0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x53, 0x74, 0x61, 0x74, 0x65, 0x66, 0x75, 0x6c,
+  0x50, 0x61, 0x72, 0x74, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x65, 0x64, 0x43, 0x61, 0x6c, 0x6c, 0x3a,
+  0x30, 0x00, 0x00, 0x00, 0x1c, 0xfb, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x05, 0x00, 0x00, 0x00, 0x68, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x02, 0x3c, 0x00, 0x00, 0x00,
+  0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x73, 0x74, 0x72, 0x69,
+  0x64, 0x65, 0x64, 0x5f, 0x73, 0x6c, 0x69, 0x63, 0x65, 0x5f, 0x32, 0x32, 0x00, 0x00, 0x00, 0x00,
+  0x26, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+  0xb4, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x02, 0x3c, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
+  0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x73, 0x74, 0x72, 0x69, 0x64, 0x65, 0x64, 0x5f,
+  0x73, 0x6c, 0x69, 0x63, 0x65, 0x5f, 0x32, 0x31, 0x00, 0x00, 0x00, 0x00, 0x72, 0xfd, 0xff, 0xff,
+  0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x05, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00,
+  0x08, 0x00, 0x07, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
+  0x38, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
+  0x73, 0x74, 0x72, 0x69, 0x64, 0x65, 0x64, 0x5f, 0x73, 0x6c, 0x69, 0x63, 0x65, 0x5f, 0x32, 0x00,
+  0xc6, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
+  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+  0x60, 0xfc, 0xff, 0xff, 0x24, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
+  0x0e, 0x00, 0x00, 0x00, 0x46, 0x75, 0x73, 0x65, 0x64, 0x43, 0x69, 0x72, 0x63, 0x6c, 0x65, 0x47,
+  0x52, 0x55, 0x00, 0x00, 0x3c, 0xfc, 0xff, 0xff, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x98, 0xfc, 0xff, 0xff, 0x48, 0x00, 0x00, 0x00,
+  0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,
+  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x2f, 0x67, 0x72, 0x75, 0x2f, 0x7a, 0x65, 0x72, 0x6f, 0x73,
+  0x00, 0x00, 0x00, 0x00, 0x4a, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
+  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
+  0xf0, 0xfc, 0xff, 0xff, 0x58, 0x01, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
+  0x0f, 0x00, 0x00, 0x00, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c,
+  0x5f, 0x31, 0x31, 0x00, 0x9a, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x2c, 0x01, 0x00, 0x00,
+  0xc0, 0xfb, 0x12, 0xbd, 0x4b, 0xb1, 0x0c, 0x3f, 0x51, 0xbe, 0xa0, 0x3d, 0xdb, 0xcd, 0xca, 0xbe,
+  0x77, 0xa7, 0x8d, 0x3e, 0xd8, 0x24, 0xe8, 0x3e, 0xc6, 0xe3, 0xfe, 0x3d, 0xa8, 0x41, 0xf0, 0xbd,
+  0x9e, 0x70, 0xf3, 0xbd, 0x50, 0xfc, 0x4b, 0x3e, 0x7f, 0x8b, 0xf0, 0x3d, 0xae, 0xc0, 0x83, 0x3d,
+  0xe4, 0xf0, 0x98, 0xbe, 0xd4, 0xd0, 0x7f, 0xbe, 0x80, 0xca, 0x98, 0x39, 0xe6, 0x2c, 0x08, 0xbe,
+  0x61, 0x44, 0xdf, 0xbd, 0x67, 0x32, 0x32, 0xbe, 0x6a, 0x61, 0xdf, 0x3e, 0xc3, 0x0c, 0x55, 0x3e,
+  0x6c, 0x28, 0x0e, 0xbf, 0xb6, 0x52, 0xf1, 0x3d, 0xb7, 0xd1, 0x3f, 0xbd, 0xa6, 0xf0, 0x9d, 0xbe,
+  0xa0, 0xdd, 0xb1, 0x3e, 0xa3, 0x7d, 0x50, 0xbd, 0x3e, 0xd7, 0xe6, 0x3e, 0xe4, 0xb0, 0xe6, 0x3d,
+  0x2a, 0xd6, 0xeb, 0x3e, 0xa8, 0xc8, 0x49, 0xbb, 0xdd, 0xdc, 0x6b, 0xbe, 0x66, 0x48, 0xc1, 0x3d,
+  0x26, 0x6e, 0x52, 0x3e, 0xfc, 0xd6, 0x64, 0x3d, 0x4f, 0x1d, 0x1f, 0xbf, 0x5f, 0xf0, 0x9e, 0x3e,
+  0xe0, 0x6e, 0xad, 0x3c, 0x48, 0x37, 0xe7, 0xbd, 0x36, 0xea, 0x0b, 0xbe, 0x3b, 0x81, 0xf2, 0xbd,
+  0x52, 0xe1, 0x56, 0xbc, 0x75, 0x2e, 0xa3, 0xbd, 0x8c, 0x71, 0xc5, 0x3d, 0xf0, 0xaf, 0x0b, 0x3e,
+  0x6b, 0x7d, 0xba, 0x3e, 0x4e, 0xbd, 0x93, 0xbe, 0xb3, 0x5c, 0x9c, 0xbe, 0x3c, 0xe2, 0xf3, 0x3c,
+  0x39, 0xf1, 0xa0, 0x3d, 0xa0, 0x35, 0x50, 0x3e, 0xfa, 0x87, 0x0e, 0xbe, 0x76, 0xc2, 0x12, 0xbd,
+  0x2a, 0xd6, 0x01, 0x3f, 0xa0, 0x77, 0xd0, 0x3c, 0x5a, 0x1f, 0x26, 0x3e, 0x02, 0x59, 0x0b, 0x3e,
+  0xef, 0x6c, 0x41, 0xbe, 0x6e, 0x40, 0x4a, 0xbd, 0x2f, 0x89, 0x33, 0x3e, 0x50, 0x54, 0x8a, 0x3e,
+  0x4d, 0xbb, 0x9f, 0xbe, 0xfd, 0x54, 0xb3, 0x3e, 0xc8, 0x5b, 0x66, 0xbe, 0xf0, 0xb0, 0x44, 0x3d,
+  0x8a, 0x4d, 0x14, 0xbe, 0x9d, 0xf7, 0xd4, 0xbd, 0x38, 0xec, 0xc7, 0xbe, 0xb2, 0x79, 0x76, 0x3e,
+  0xb2, 0xc2, 0xdd, 0xbe, 0x44, 0xd9, 0x05, 0xbe, 0x59, 0x34, 0x89, 0x3e, 0x71, 0xf8, 0x2b, 0x3e,
+  0x1d, 0x62, 0x24, 0x3f, 0x40, 0xe6, 0x02, 0x3d, 0xef, 0x03, 0xb8, 0x3d, 0x02, 0x00, 0x00, 0x00,
+  0x0f, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x58, 0xfe, 0xff, 0xff, 0x98, 0x01, 0x00, 0x00,
+  0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x77, 0x68, 0x69, 0x6c,
+  0x65, 0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00,
+  0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x68, 0x01, 0x00, 0x00, 0x9c, 0xb9, 0x96, 0xbe,
+  0x30, 0x84, 0xdc, 0x3e, 0xb8, 0xe2, 0xc0, 0x3d, 0x00, 0xce, 0xf1, 0x3a, 0x28, 0xd6, 0xfb, 0x3d,
+  0x49, 0x84, 0x95, 0xbe, 0xcc, 0x0d, 0x52, 0x3e, 0x7c, 0x4e, 0x6e, 0xbe, 0xde, 0xda, 0x4c, 0xbe,
+  0x84, 0x5e, 0xda, 0x3e, 0x46, 0x2b, 0xd1, 0x3e, 0x78, 0xc8, 0x71, 0xbe, 0x00, 0xfd, 0x53, 0x3d,
+  0x28, 0x4e, 0x91, 0x3e, 0x00, 0x46, 0xd6, 0xba, 0x20, 0x06, 0x97, 0xbe, 0xf4, 0x04, 0xdc, 0xbe,
+  0xde, 0xf8, 0x05, 0xbf, 0x62, 0x20, 0x1d, 0xbe, 0x28, 0x28, 0xf9, 0x3d, 0xc6, 0xa0, 0x86, 0xbe,
+  0xa2, 0x2f, 0x7f, 0xbe, 0xa0, 0xa1, 0x1d, 0xbd, 0x3c, 0x03, 0xb2, 0x3e, 0xe6, 0xe6, 0x7c, 0xbe,
+  0x2e, 0x37, 0xbe, 0xbe, 0x84, 0xb2, 0x86, 0xbd, 0x10, 0x19, 0x56, 0x3e, 0x59, 0x86, 0x01, 0x3f,
+  0xfc, 0x54, 0x15, 0x3e, 0xc3, 0xbd, 0x07, 0x3f, 0xa0, 0xcb, 0x5f, 0x3e, 0x6c, 0x19, 0xbb, 0x3e,
+  0x9c, 0x98, 0x24, 0xbe, 0x40, 0x57, 0xd1, 0xbc, 0xb0, 0x9c, 0xec, 0xbd, 0x90, 0x19, 0xb4, 0x3d,
+  0x59, 0x11, 0xe7, 0xbe, 0x04, 0x11, 0xd7, 0xbd, 0x6a, 0xd8, 0x46, 0xbe, 0xb9, 0xf2, 0x01, 0xbf,
+  0x40, 0xe0, 0x2e, 0xbd, 0x9e, 0xe6, 0x9a, 0x3e, 0xa0, 0x27, 0xda, 0xbe, 0x39, 0xe9, 0x04, 0x3f,
+  0x5c, 0x2f, 0x2d, 0x3e, 0x18, 0x35, 0x95, 0x3e, 0x5c, 0x67, 0x14, 0x3e, 0xd0, 0xb1, 0x92, 0xbd,
+  0xa8, 0x99, 0xe2, 0xbd, 0x00, 0x1e, 0x0e, 0x3e, 0x80, 0x85, 0x7a, 0x3c, 0x88, 0xde, 0xde, 0x3e,
+  0x0a, 0x10, 0xc9, 0x3e, 0x28, 0x29, 0x3c, 0xbd, 0xbe, 0x3a, 0xfd, 0x3e, 0x36, 0x76, 0xef, 0xbe,
+  0x6e, 0x44, 0xb4, 0x3e, 0xdc, 0xd6, 0x9c, 0xbd, 0xf0, 0xed, 0x9a, 0x3e, 0x90, 0x9c, 0x6b, 0x3d,
+  0x0c, 0xc3, 0x32, 0x3e, 0x8a, 0x27, 0x1f, 0xbe, 0x00, 0x64, 0x5f, 0x3a, 0x8e, 0x71, 0xcc, 0x3e,
+  0xcf, 0xe7, 0xe1, 0xbe, 0xc6, 0x65, 0xb4, 0x3e, 0xa4, 0x65, 0x6d, 0x3e, 0x31, 0xd8, 0x03, 0x3f,
+  0x2c, 0x2a, 0xa8, 0xbd, 0x38, 0x1b, 0xac, 0x3e, 0x60, 0xcc, 0x64, 0x3e, 0x18, 0x4c, 0x0e, 0xbd,
+  0x82, 0x5e, 0xa2, 0x3e, 0xde, 0x70, 0xb0, 0xbe, 0x46, 0x07, 0xe6, 0xbe, 0xf6, 0x4a, 0xa8, 0xbe,
+  0x90, 0xfa, 0x3f, 0x3e, 0x5c, 0x9a, 0xe9, 0xbe, 0x63, 0x1e, 0xd3, 0xbe, 0x20, 0x74, 0x7e, 0x3d,
+  0x20, 0x9c, 0x02, 0xbf, 0xf7, 0x65, 0x02, 0x3f, 0xb6, 0x45, 0xdf, 0x3e, 0x4e, 0xc2, 0x48, 0xbe,
+  0xe3, 0x90, 0xa9, 0xbe, 0xc8, 0x36, 0xab, 0x3d, 0xca, 0xc0, 0x22, 0xbe, 0xec, 0x99, 0x26, 0x3e,
+  0xd0, 0x91, 0x35, 0x3e, 0x02, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
+  0x0c, 0x00, 0x10, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x0c, 0x00, 0x00, 0x00,
+  0x28, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00,
+  0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f,
+  0x78, 0x3a, 0x30, 0x00, 0xec, 0xff, 0xff, 0xff, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+  0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00};
+
+const std::vector<float> input_data = {7.899295,   -4.584313,  -2.9251342,
+                                       -2.1820352, -10.649105, 1.3530581};
+
+const std::vector<float> reference_output_data = {-0.9979859, -0.90550894, 0.025957875, -0.39570245,
+                                                  -0.8868108};
+
+} // namespace gru_float
+
+class TestDataFloatGRU : public TestDataGRUBase<float>
+{
+public:
+  TestDataFloatGRU()
+  {
+    _input_data = gru_float::input_data;
+    _reference_output_data = gru_float::reference_output_data;
+    _test_kernel_model_circle = gru_float::test_kernel_model_circle;
+  }
+
+  ~TestDataFloatGRU() override = default;
+};
+
+} // namespace test_model
+} // namespace onert_micro
+
+#endif // ONERT_MICRO_TEST_MODELS_FLOAT_GRU_KERNEL_H
diff --git a/onert-micro/onert-micro/include/test_models/gru/TestDataGRUBase.h b/onert-micro/onert-micro/include/test_models/gru/TestDataGRUBase.h
new file mode 100644
index 00000000000..5c3da425d4a
--- /dev/null
+++ b/onert-micro/onert-micro/include/test_models/gru/TestDataGRUBase.h
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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 ONERT_MICRO_TEST_MODELS_GRU_KERNEL_BASE_H
+#define ONERT_MICRO_TEST_MODELS_GRU_KERNEL_BASE_H
+
+#include "test_models/TestDataBase.h"
+
+namespace onert_micro
+{
+namespace test_model
+{
+
+template <typename T> class TestDataGRUBase : public TestDataBase<T>
+{
+public:
+  TestDataGRUBase() = default;
+
+  const unsigned char *get_model_ptr() override final { return _test_kernel_model_circle; }
+
+  const std::vector<T> &get_input_data_by_index(int i) override final
+  {
+    switch (i)
+    {
+      case 0:
+        return _input_data;
+      default:
+        assert(false && "Wrong input index");
+    }
+  }
+
+  const std::vector<T> &get_output_data_by_index(int i) override final
+  {
+    assert(i == 0);
+    return _reference_output_data;
+  }
+
+protected:
+  std::vector<T> _input_data;
+  std::vector<T> _reference_output_data;
+  const unsigned char *_test_kernel_model_circle;
+};
+
+} // namespace test_model
+} // namespace onert_micro
+
+#endif // ONERT_MICRO_TEST_MODELS_GRU_KERNEL_BASE_H
diff --git a/onert-micro/onert-micro/src/OMInterpreter.cpp b/onert-micro/onert-micro/src/OMInterpreter.cpp
index 1e7ee420d4a..bb7076f6416 100644
--- a/onert-micro/onert-micro/src/OMInterpreter.cpp
+++ b/onert-micro/onert-micro/src/OMInterpreter.cpp
@@ -27,7 +27,7 @@ OMStatus OMInterpreter::importModel(const char *model_ptr, const OMConfig &confi
   return _runtime_module.importModel(model_ptr, config);
 }
 
-OMStatus OMInterpreter::run() { return _runtime_module.run(); }
+OMStatus OMInterpreter::run(const OMConfig &config) { return _runtime_module.run(config); }
 
 OMStatus OMInterpreter::reset() { return _runtime_module.reset(); }
 
diff --git a/onert-micro/onert-micro/src/api/onert-micro.cpp b/onert-micro/onert-micro/src/api/onert-micro.cpp
index 538529b32b9..37f2d63e611 100644
--- a/onert-micro/onert-micro/src/api/onert-micro.cpp
+++ b/onert-micro/onert-micro/src/api/onert-micro.cpp
@@ -307,7 +307,7 @@ NNFW_STATUS nnfw_session::train_run(bool update_weights)
     float *user_input_data = (float *)_train_interpreter->getInputData(0);
     memcpy(allocated_input_data, user_input_data,
            sizeof(float) * _train_interpreter->getInputSizeAt(0));
-    _train_interpreter->run();
+    _train_interpreter->run(_config);
     float *calculated_ptr = (float *)_train_interpreter->getOutputDataAt(0);
     memcpy(outputbuf, calculated_ptr, sizeof(float) * _train_interpreter->getOutputSizeAt(0));
     _train_interpreter->reset();
@@ -380,7 +380,7 @@ NNFW_STATUS nnfw_session::train_get_loss(uint32_t index, float *loss)
       break;
   }
 
-  _train_interpreter->evaluateMetric(m, reinterpret_cast<void *>(loss),
+  _train_interpreter->evaluateMetric(_config, m, reinterpret_cast<void *>(loss),
                                      _config.training_context.batch_size);
   return NNFW_STATUS_NO_ERROR;
 }
diff --git a/onert-micro/onert-micro/src/core/OMRuntimeModule.cpp b/onert-micro/onert-micro/src/core/OMRuntimeModule.cpp
index 857d7413240..f1b1a5b0a1e 100644
--- a/onert-micro/onert-micro/src/core/OMRuntimeModule.cpp
+++ b/onert-micro/onert-micro/src/core/OMRuntimeModule.cpp
@@ -148,7 +148,7 @@ OMStatus OMRuntimeModule::allocateInputs()
   return _graphs.at(0).allocateGraphInputs();
 }
 
-OMStatus OMRuntimeModule::run()
+OMStatus OMRuntimeModule::run(const OMConfig &config)
 {
   OMStatus status = Ok;
 
@@ -158,7 +158,11 @@ OMStatus OMRuntimeModule::run()
   core::OMRuntimeGraph &main_graph = _graphs.at(0);
 
   execute::OMExecuteArgs execute_args = {main_graph.getRuntimeStorage(),
-                                         main_graph.getRuntimeContext(), 0, *this};
+                                         main_graph.getRuntimeContext(),
+                                         0,
+                                         *this,
+                                         config.training_context.num_of_train_layers,
+                                         config.train_mode};
 
   status = execute::OMKernelExecute::runForward(execute_args, main_graph.getRuntimeAllocator());
   if (status != Ok)
diff --git a/onert-micro/onert-micro/src/core/OMTrainingRuntimeModule.cpp b/onert-micro/onert-micro/src/core/OMTrainingRuntimeModule.cpp
index 87e3c27aef1..c3492956539 100644
--- a/onert-micro/onert-micro/src/core/OMTrainingRuntimeModule.cpp
+++ b/onert-micro/onert-micro/src/core/OMTrainingRuntimeModule.cpp
@@ -126,7 +126,7 @@ OMStatus OMTrainingRuntimeModule::trainSingleStep(OMConfig &config)
 
     //  b. Run forward graph
     {
-      status = run();
+      status = run(config);
       assert(status == Ok);
       if (status != Ok)
         return status;
@@ -203,8 +203,8 @@ OMStatus OMTrainingRuntimeModule::trainSingleStep(OMConfig &config)
  *              after calculation for current batch_num (the sequence number of the current sample)
  *              this value is added to metric_val
  */
-OMStatus OMTrainingRuntimeModule::evaluateMetric(OMMetrics metric, void *metric_val,
-                                                 uint32_t test_size)
+OMStatus OMTrainingRuntimeModule::evaluateMetric(const OMConfig &config, OMMetrics metric,
+                                                 void *metric_val, uint32_t test_size)
 {
   OMStatus status = Ok;
   OMRuntimeGraph &forward_main_graph = _graphs.at(0);
@@ -234,7 +234,7 @@ OMStatus OMTrainingRuntimeModule::evaluateMetric(OMMetrics metric, void *metric_
 
     //  b. Run forward graph
     {
-      status = run();
+      status = run(config);
       assert(status == Ok);
       if (status != Ok)
         return status;
diff --git a/onert-micro/onert-micro/src/execute/kernels/GRU.cpp b/onert-micro/onert-micro/src/execute/kernels/GRU.cpp
new file mode 100644
index 00000000000..0a86e6c188e
--- /dev/null
+++ b/onert-micro/onert-micro/src/execute/kernels/GRU.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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.
+ */
+
+#include <core/OMDataType.h>
+#include "OMStatus.h"
+
+#include "core/OMUtils.h"
+#include "core/OMKernelData.h"
+#include "core/memory/OMMemoryManager.h"
+
+#include "execute/OMKernelExecutionBuilder.h"
+#include "execute/OMUtils.h"
+#include "execute/OMRuntimeKernel.h"
+
+#include "PALGRU.h"
+
+using namespace onert_micro;
+using namespace onert_micro::core;
+using namespace onert_micro::execute;
+
+namespace
+{
+
+constexpr uint32_t inputTensorIdx = 0;
+constexpr uint32_t hiddenHiddenTensorIdx = 1;
+constexpr uint32_t hiddenHiddenBiasTensorIdx = 2;
+constexpr uint32_t hiddenInputTensorIdx = 3;
+constexpr uint32_t hiddenInputBiasTensorIdx = 4;
+constexpr uint32_t stateTensorIdx = 5;
+
+constexpr uint32_t outputTensorIdx = 0;
+
+} // namespace
+
+// NOTE: doesnt currently support dynamic shapes
+OMStatus onert_micro::execute::execute_kernel_CircleGRU(const OMExecuteArgs &execute_args)
+{
+  core::OMRuntimeContext &runtime_context = execute_args.runtime_context;
+  core::OMRuntimeStorage &runtime_storage = execute_args.runtime_storage;
+  uint16_t op_index = execute_args.kernel_index;
+
+  const circle::Tensor *input;
+  const circle::Tensor *hidden_hidden;
+  const circle::Tensor *hidden_hidden_bias;
+  const circle::Tensor *hidden_input;
+  const circle::Tensor *hidden_input_bias;
+  const circle::Tensor *state;
+
+  const circle::Tensor *output;
+
+  uint8_t *input_data;
+  uint8_t *hidden_hidden_data;
+  uint8_t *hidden_hidden_bias_data;
+  uint8_t *hidden_input_data;
+  uint8_t *hidden_input_bias_data;
+  uint8_t *state_data;
+  uint8_t *output_data;
+
+  uint16_t state_tensor_index = 0;
+
+  // Read kernel
+  {
+    execute::OMRuntimeKernel runtime_kernel;
+    runtime_kernel.readKernel(op_index, runtime_context);
+
+    input = runtime_kernel.inputs[inputTensorIdx];
+    hidden_hidden = runtime_kernel.inputs[hiddenHiddenTensorIdx];
+    hidden_hidden_bias = runtime_kernel.inputs[hiddenHiddenBiasTensorIdx];
+    hidden_input = runtime_kernel.inputs[hiddenInputTensorIdx];
+    hidden_input_bias = runtime_kernel.inputs[hiddenInputBiasTensorIdx];
+    state = runtime_kernel.inputs[stateTensorIdx];
+
+    output = runtime_kernel.outputs[outputTensorIdx];
+    assert(input != nullptr);
+    assert(hidden_hidden != nullptr);
+    assert(hidden_input != nullptr);
+    assert(state != nullptr);
+    // Biases can be nullptr
+    assert(output != nullptr);
+
+    runtime_kernel.getDataFromStorage(op_index, runtime_storage, runtime_context);
+
+    input_data = runtime_kernel.inputs_data[inputTensorIdx];
+    hidden_hidden_data = runtime_kernel.inputs_data[hiddenHiddenTensorIdx];
+    hidden_hidden_bias_data = runtime_kernel.inputs_data[hiddenHiddenBiasTensorIdx];
+    hidden_input_data = runtime_kernel.inputs_data[hiddenInputTensorIdx];
+    hidden_input_bias_data = runtime_kernel.inputs_data[hiddenInputBiasTensorIdx];
+    state_data = runtime_kernel.inputs_data[stateTensorIdx];
+
+    output_data = runtime_kernel.outputs_data[outputTensorIdx];
+    assert(input_data != nullptr);
+    assert(hidden_hidden_data != nullptr);
+    assert(hidden_input_data != nullptr);
+    assert(state_data != nullptr);
+    // Bias can be nullptr
+    assert(output_data != nullptr);
+
+    state_tensor_index = runtime_kernel.inputs_index[stateTensorIdx];
+  }
+
+  OMStatus status;
+
+  uint8_t *output_hidden_data;
+  uint8_t *output_input_data;
+
+  status =
+    core::memory::OMMemoryManager::allocateMemory(core::OMRuntimeShape(hidden_hidden).flatSize() *
+                                                    sizeof(core::OMDataType(hidden_hidden->type())),
+                                                  &output_hidden_data);
+  if (status != Ok)
+    return status;
+  status = core::memory::OMMemoryManager::allocateMemory(
+    core::OMRuntimeShape(hidden_input).flatSize() * sizeof(core::OMDataType(hidden_input->type())),
+    &output_input_data);
+  if (status != Ok)
+    return status;
+
+  // If train mode need to allocate memory for internal intermediate tensors for calculation
+  // gradients further Number of intermediate tensors
+  const int32_t num_of_intermediate_tensors = 9;
+  // Note: size of the intermediate is equal to output size (should be checked during import phase)
+  const int32_t size_of_intermediate_tensors = core::OMRuntimeShape(output).flatSize();
+  assert(size_of_intermediate_tensors > 0);
+  if (size_of_intermediate_tensors == 0)
+    return UnknownError;
+
+  const int32_t input_size = core::OMRuntimeShape(input).flatSize();
+  const int32_t output_size = size_of_intermediate_tensors;
+
+  // Allocate buffer with following schema:
+  // times * [output_size * sizeof(data_type),
+  // num_of_intermediate_tensors * size_of_intermediate_tensors * sizeof(data_type)]
+  // Note: need to save all necessary intermediate data to calculate gradients
+  // Deallocation should perform train/GRU kernel
+  const size_t data_type_size = sizeof(core::OMDataType(input->type()));
+  const int32_t time = OMRuntimeShape(input).dims(0);
+  size_t intermediate_buffer_size = 0;
+  uint8_t *intermediate_buffer = nullptr;
+  if (execute_args.is_train_mode)
+  {
+    const auto num_operators = runtime_context.getCircleOperators()->size();
+
+    uint32_t num_train_layers =
+      execute_args.num_train_layers == 0 ? num_operators : execute_args.num_train_layers;
+    uint32_t last_node_pos = std::min(num_operators, num_train_layers);
+    uint32_t last_train_op_index = num_operators - last_node_pos;
+
+    if (execute_args.kernel_index >= last_train_op_index)
+    {
+      intermediate_buffer_size = num_of_intermediate_tensors * size_of_intermediate_tensors;
+
+      status = core::memory::OMMemoryManager::allocateMemory(
+        time * intermediate_buffer_size * data_type_size, &intermediate_buffer);
+      if (status != Ok)
+        return status;
+
+      // Save its buffer to state tensor index
+      runtime_storage.saveDataToTensorIndex(intermediate_buffer, state_tensor_index);
+    }
+  }
+
+  switch (input->type())
+  {
+#ifndef DIS_FLOAT
+    case circle::TensorType_FLOAT32:
+    {
+      status =
+        pal::GRU(core::utils::castInputData<float>(input_data),
+                 core::utils::castInputData<float>(hidden_input_data),
+                 core::utils::castInputData<float>(hidden_hidden_data),
+                 core::utils::castInputData<float>(hidden_input_bias_data),
+                 core::utils::castInputData<float>(hidden_hidden_bias_data),
+                 core::utils::castInputData<float>(state_data),
+                 core::utils::castOutputData<float>(output_data),
+                 core::utils::castOutputData<float>(output_input_data),
+                 core::utils::castOutputData<float>(output_hidden_data),
+                 core::OMRuntimeShape(input), core::OMRuntimeShape(output),
+                 core::OMRuntimeShape(hidden_input), core::OMRuntimeShape(hidden_hidden),
+                 intermediate_buffer_size, core::utils::castOutputData<float>(intermediate_buffer));
+    }
+    break;
+#endif // DIS_FLOAT
+    default:
+    {
+      status = UnsupportedType;
+      assert(false && "Unsupported type.");
+    }
+  }
+
+  core::memory::OMMemoryManager::deallocateMemory(output_input_data);
+  core::memory::OMMemoryManager::deallocateMemory(output_hidden_data);
+
+  return status;
+}
diff --git a/onert-micro/onert-micro/src/execute/kernels/tests/GRU.test.cpp b/onert-micro/onert-micro/src/execute/kernels/tests/GRU.test.cpp
new file mode 100644
index 00000000000..d9d49621947
--- /dev/null
+++ b/onert-micro/onert-micro/src/execute/kernels/tests/GRU.test.cpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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.
+ */
+
+#include "execute/OMTestUtils.h"
+#include "test_models/gru/FloatGRUKernel.h"
+
+namespace onert_micro
+{
+namespace execute
+{
+namespace testing
+{
+
+using namespace testing;
+
+class GRUTest : public ::testing::Test
+{
+  // Do nothing
+};
+
+TEST_F(GRUTest, Float_P)
+{
+  onert_micro::test_model::TestDataFloatGRU test_data_kernel;
+  std::vector<float> output_data_vector =
+    onert_micro::execute::testing::checkKernel<float>(1, &test_data_kernel);
+  EXPECT_THAT(output_data_vector,
+              FloatArrayNear(test_data_kernel.get_output_data_by_index(0), 0.0001f));
+}
+
+} // namespace testing
+} // namespace execute
+} // namespace onert_micro
diff --git a/onert-micro/onert-micro/src/import/OMExecutionPlanCreator.cpp b/onert-micro/onert-micro/src/import/OMExecutionPlanCreator.cpp
index a83efa06fa1..5aad12f25a2 100644
--- a/onert-micro/onert-micro/src/import/OMExecutionPlanCreator.cpp
+++ b/onert-micro/onert-micro/src/import/OMExecutionPlanCreator.cpp
@@ -34,6 +34,7 @@ bool isTrainableWeights(const circle::OperatorCode *opcode)
   {
     case circle::BuiltinOperator_FULLY_CONNECTED:
     case circle::BuiltinOperator_CONV_2D:
+    case circle::BuiltinOperator_GRU:
       return true;
     default:
       return false;
@@ -255,8 +256,9 @@ OMStatus OMExecutionPlanCreator::createForwardExecutionPlan(
     // of the graph)
     bool need_to_save_input_data =
       (index >= last_train_op_indx) and
-      ((trainable_ops_config.find(index) != trainable_ops_config.end() and
-        trainable_ops_config[index] != ONLY_BIAS) or
+      ((trainable_ops_config.empty() or
+        trainable_ops_config.find(index) != trainable_ops_config.end() and
+          trainable_ops_config[index] != ONLY_BIAS) or
        isOpNeedSaveInputData(opcode));
 
     // Flag to determine is current operation needed to save output data (is this op in training
diff --git a/onert-micro/onert-micro/src/import/kernels/GRU.cpp b/onert-micro/onert-micro/src/import/kernels/GRU.cpp
new file mode 100644
index 00000000000..2c1167c98b5
--- /dev/null
+++ b/onert-micro/onert-micro/src/import/kernels/GRU.cpp
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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.
+ */
+
+#include "OMStatus.h"
+
+#include "import/OMKernelConfigureBuilder.h"
+
+#include "core/OMUtils.h"
+#include "core/OMKernelData.h"
+
+#include "execute/OMRuntimeKernel.h"
+
+using namespace onert_micro;
+using namespace onert_micro::core;
+
+namespace
+{
+
+constexpr uint32_t inputTensorIdx = 0;
+constexpr uint32_t hiddenHiddenTensorIdx = 1;
+constexpr uint32_t hiddenHiddenBiasTensorIdx = 2;
+constexpr uint32_t hiddenInputTensorIdx = 3;
+constexpr uint32_t hiddenInputBiasTensorIdx = 4;
+constexpr uint32_t stateTensorIdx = 5;
+
+constexpr uint32_t outputTensorIdx = 0;
+
+} // namespace
+
+OMStatus onert_micro::import::configure_kernel_CircleGRU(const OMConfigureArgs &config_args)
+{
+  core::OMRuntimeContext &runtime_context = config_args.runtime_context;
+  uint16_t op_index = config_args.kernel_index;
+
+  const circle::Tensor *input;
+  const circle::Tensor *hidden_hidden;
+  const circle::Tensor *hidden_hidden_bias;
+  const circle::Tensor *hidden_input;
+  const circle::Tensor *hidden_input_bias;
+  const circle::Tensor *state;
+
+  const circle::Tensor *output;
+
+  // Read kernel
+  execute::OMRuntimeKernel runtime_kernel;
+  runtime_kernel.readKernel(op_index, runtime_context);
+
+  input = runtime_kernel.inputs[inputTensorIdx];
+  hidden_hidden = runtime_kernel.inputs[hiddenHiddenTensorIdx];
+  hidden_hidden_bias = runtime_kernel.inputs[hiddenHiddenBiasTensorIdx];
+  hidden_input = runtime_kernel.inputs[hiddenInputTensorIdx];
+  hidden_input_bias = runtime_kernel.inputs[hiddenInputBiasTensorIdx];
+  state = runtime_kernel.inputs[stateTensorIdx];
+
+  output = runtime_kernel.outputs[outputTensorIdx];
+  assert(input != nullptr);
+  assert(hidden_hidden != nullptr);
+  assert(hidden_input != nullptr);
+  assert(state != nullptr);
+  // Biases can be nullptr
+  assert(output != nullptr);
+
+  OMStatus status = Ok;
+
+  OMRuntimeShape hidden_hidden_shape(hidden_hidden);
+  OMRuntimeShape hidden_input_shape(hidden_input);
+  OMRuntimeShape output_shape(output);
+  OMRuntimeShape state_shape(state);
+
+  status = utils::checkCondition(hidden_hidden_shape.dims(0) == hidden_input_shape.dims(0));
+  if (status != Ok)
+    return status;
+
+  const int32_t div_factor = 3;
+  status =
+    utils::checkCondition(hidden_hidden_shape.dims(0) ==
+                          (div_factor * output_shape.dims(output_shape.dimensionsCount() - 1)));
+  if (status != Ok)
+    return status;
+
+  status = utils::checkCondition(output_shape.dims(output_shape.dimensionsCount() - 1) ==
+                                 state_shape.dims(state_shape.dimensionsCount() - 1));
+  if (status != Ok)
+    return status;
+
+  status = utils::checkCondition(input->type() == output->type());
+  if (status != Ok)
+    return status;
+
+  return status;
+}