From 6ab94e0b318884bbcb95e2ea3835f951502e1d99 Mon Sep 17 00:00:00 2001 From: jaseg Date: Wed, 14 Oct 2020 12:47:28 +0200 Subject: Move firmware into subdirectory --- .../ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp | 221 +++++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp (limited to 'fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp') diff --git a/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp b/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp new file mode 100644 index 0000000..efb3257 --- /dev/null +++ b/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp @@ -0,0 +1,221 @@ +/* ---------------------------------------------------------------------- +* 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: + * 
z[t] = sigmoid( W_z ⋅ {h[t-1],x[t]} )
+ * r[t] = sigmoid( W_r ⋅ {h[t-1],x[t]} ) 
+ * n[t] = tanh( W_n ⋅ [r[t] × {h[t-1], x[t]} ) 
+ * h[t] = (1 - z[t]) × h[t-1] + z[t] × n[t]
+ * \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() + * + * Refer + * \link arm_nnexamples_gru.cpp \endlink + * + */ + +#include +#include +#include +#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; +} -- cgit