Trinity Devboard PCB V1.0 Firmware. FreeRTOS is setup and the MCU reads IMU data over SPI fand Magnetometer data over I2C, each with a seperate task. Sensordata is then run though MadgwickAHRS and send over USB as serial packet data to use in trinity visualizer. Bare minimum functionality works and is replicated from the first prototype.

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+/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project:       CMSIS NN Library
+* Title:         arm_nnexamples_gru.cpp
+*
+* Description:   Gated Recurrent Unit Example
+*
+* Target Processor: Cortex-M4/Cortex-M7
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+*   - Redistributions of source code must retain the above copyright
+*     notice, this list of conditions and the following disclaimer.
+*   - Redistributions in binary form must reproduce the above copyright
+*     notice, this list of conditions and the following disclaimer in
+*     the documentation and/or other materials provided with the
+*     distribution.
+*   - Neither the name of Arm LIMITED nor the names of its contributors
+*     may be used to endorse or promote products derived from this
+*     software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup GRUExample Gated Recurrent Unit Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates a gated recurrent unit (GRU) example with the use of fully-connected,
+ * Tanh/Sigmoid activation functions.
+ *
+ * \par Model definition:
+ * \par
+ * GRU is a type of recurrent neural network (RNN). It contains two sigmoid gates and one hidden
+ * state. 
+ * \par
+ * The computation can be summarized as:
+ * <pre>z[t] = sigmoid( W_z &sdot; {h[t-1],x[t]} )
+ * r[t] = sigmoid( W_r &sdot; {h[t-1],x[t]} ) 
+ * n[t] = tanh( W_n &sdot; [r[t] &times; {h[t-1], x[t]} ) 
+ * h[t] = (1 - z[t]) &times; h[t-1] + z[t] &times; n[t] </pre>
+ * \image html GRU.gif "Gate Recurrent Unit Diagram"
+ *
+ * \par Variables Description:
+ * \par
+ * \li \c update_gate_weights, \c reset_gate_weights, \c hidden_state_weights are weights corresponding to update gate (W_z), reset gate (W_r), and hidden state (W_n).
+ * \li \c update_gate_bias, \c reset_gate_bias, \c hidden_state_bias are layer bias arrays
+ * \li \c test_input1, \c test_input2, \c test_history are the inputs and initial history
+ *
+ * \par
+ * The buffer is allocated as:
+ * \par
+ * | reset | input | history | update | hidden_state |
+ * \par
+ * In this way, the concatination is automatically done since (reset, input) and (input, history)
+ * are physically concatinated in memory.
+ * \par
+ *  The ordering of the weight matrix should be adjusted accordingly.
+ *
+  *
+ * 
+ * \par CMSIS DSP Software Library Functions Used:
+ * \par
+ * - arm_fully_connected_mat_q7_vec_q15_opt()
+ * - arm_nn_activations_direct_q15()
+ * - arm_mult_q15()
+ * - arm_offset_q15()
+ * - arm_sub_q15()
+ * - arm_copy_q15()
+ *
+ * <b> Refer  </b>
+ * \link arm_nnexamples_gru.cpp \endlink
+ *
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include "arm_nnexamples_gru_test_data.h"
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#ifdef _RTE_
+#include "RTE_Components.h"
+#ifdef RTE_Compiler_EventRecorder
+#include "EventRecorder.h"
+#endif
+#endif
+
+#define DIM_HISTORY 32
+#define DIM_INPUT 32
+#define DIM_VEC 64
+
+#define USE_X4
+
+#ifndef USE_X4
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X2;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X2;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X2;
+#else
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X4;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X4;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X4;
+#endif
+
+static q7_t update_gate_bias[DIM_HISTORY] = UPDATE_GATE_BIAS;
+static q7_t reset_gate_bias[DIM_HISTORY] = RESET_GATE_BIAS;
+static q7_t hidden_state_bias[DIM_HISTORY] = HIDDEN_STATE_BIAS;
+
+static q15_t test_input1[DIM_INPUT] = INPUT_DATA1;
+static q15_t test_input2[DIM_INPUT] = INPUT_DATA2;
+static q15_t test_history[DIM_HISTORY] = HISTORY_DATA;
+
+q15_t     scratch_buffer[DIM_HISTORY * 4 + DIM_INPUT];
+
+void gru_example(q15_t * scratch_input, uint16_t input_size, uint16_t history_size,
+                 q7_t * weights_update, q7_t * weights_reset, q7_t * weights_hidden_state,
+                 q7_t * bias_update, q7_t * bias_reset, q7_t * bias_hidden_state)
+{
+  q15_t    *reset = scratch_input;
+  q15_t    *input = scratch_input + history_size;
+  q15_t    *history = scratch_input + history_size + input_size;
+  q15_t    *update = scratch_input + 2 * history_size + input_size;
+  q15_t    *hidden_state = scratch_input + 3 * history_size + input_size;
+
+  // reset gate calculation
+  // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(input, weights_reset, input_size + history_size, history_size, 0, 15, bias_reset,
+                                     reset, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(input, weights_reset, input_size + history_size, history_size, 0, 15,
+                                         bias_reset, reset, NULL);
+#endif
+  // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(reset, history_size, 0, ARM_SIGMOID);
+  arm_mult_q15(history, reset, reset, history_size);
+
+  // update gate calculation
+  // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(input, weights_update, input_size + history_size, history_size, 0, 15,
+                                     bias_update, update, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(input, weights_update, input_size + history_size, history_size, 0, 15,
+                                         bias_update, update, NULL);
+#endif
+
+  // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(update, history_size, 0, ARM_SIGMOID);
+
+  // hidden state calculation
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+                                     bias_hidden_state, hidden_state, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+                                         bias_hidden_state, hidden_state, NULL);
+#endif
+
+  // tanh function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(hidden_state, history_size, 0, ARM_TANH);
+  arm_mult_q15(update, hidden_state, hidden_state, history_size);
+
+  // we calculate z - 1 here
+  // so final addition becomes substraction
+  arm_offset_q15(update, 0x8000, update, history_size);
+  // multiply history
+  arm_mult_q15(history, update, update, history_size);
+  // calculate history_out
+  arm_sub_q15(hidden_state, update, history, history_size);
+
+  return;
+}
+
+int main()
+{
+  #ifdef RTE_Compiler_EventRecorder
+  EventRecorderInitialize (EventRecordAll, 1);  // initialize and start Event Recorder
+  #endif
+
+  printf("Start GRU execution\n");
+  int       input_size = DIM_INPUT;
+  int       history_size = DIM_HISTORY;
+
+  // copy over the input data 
+  arm_copy_q15(test_input1, scratch_buffer + history_size, input_size);
+  arm_copy_q15(test_history, scratch_buffer + history_size + input_size, history_size);
+
+  gru_example(scratch_buffer, input_size, history_size,
+              update_gate_weights, reset_gate_weights, hidden_state_weights,
+              update_gate_bias, reset_gate_bias, hidden_state_bias);
+  printf("Complete first iteration on GRU\n");
+
+  arm_copy_q15(test_input2, scratch_buffer + history_size, input_size);
+  gru_example(scratch_buffer, input_size, history_size,
+              update_gate_weights, reset_gate_weights, hidden_state_weights,
+              update_gate_bias, reset_gate_bias, hidden_state_bias);
+  printf("Complete second iteration on GRU\n");
+
+  return 0;
+}
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+CMSIS NN Lib example arm_nnexample_gru0 for
+  Cortex-M0, Cortex-M3, Cortex-M4 and Cortex-M7.
+
+The example is configured for IAR Embedded Workbench for ARM Simulator.
+
+When changing target, remember to change the ARM_MATH_CMx and __FPU_PRESENT
+Preprocessor defines for C/C++ Compiler