ailayer_dense_cmsis.h File Reference

Arm CMSIS implementation of the Dense layer for Arm Cortex processors. More...

Go to the source code of this file.

Functions
ailayer_t *	ailayer_dense_f32_cmsis (ailayer_dense_t *layer, ailayer_t *input_layer)
	Initializes and connect a Dense layer with the F32 CMSIS implementation. More...
ailayer_t *	ailayer_dense_wt_q7_cmsis (ailayer_dense_t *layer, ailayer_t *input_layer)
	Initializes and connect a Dense layer with the Q7 AMR CMSIS implementation for transposed weights tensor. More...

Detailed Description

Arm CMSIS implementation of the Dense layer for Arm Cortex processors.

Version

2.2.0

Copyright

Copyright (C) 2020-2023 Fraunhofer Institute for Microelectronic Circuits and Systems. All rights reserved.

AIfES is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Arm CMSIS implementations of the Dense layer in F32 , Q31 and Q7 data-type. These implementations are specifically designed for the Arm Cortex processors and take advantage of SIMD instructions. For more information about the Dense layer refer to ailayer_dense.h.

Function Documentation

ailayer_dense_f32_cmsis()

ailayer_t* ailayer_dense_f32_cmsis	(	ailayer_dense_t *	layer,
		ailayer_t *	input_layer
	)

Initializes and connect a Dense layer with the F32 CMSIS implementation.

Example: Create the layer structure with pretrained weights:

// Use constant data only for inference. For training remove the const qualifier!!
const float weights_data_dense[] = {-10.1164f, -8.4212f, 5.4396f, 7.297f, -7.6482f, -9.0155f};
const float bias_data_dense[] = {-2.9653f,  2.3677f, -1.5968f};
ailayer_dense_f32_t dense_layer = {
    .neurons = 3,
    .weights.data = (float *) weights_data_dense,
    .bias.data = (float *) bias_data_dense
};

Example: Create the layer structure for training:

ailayer_dense_f32_t dense_layer = {
    .neurons = 3
};

Example: Initialize and connect the layer:

x = ailayer_dense_f32_cmsis(&dense_layer, x);

Parameters

*layer	The layer structure to initialize.
*input_layer	The prior layer.

Returns

The (successfully) initialized layer structure.

ailayer_dense_wt_q7_cmsis()

ailayer_t* ailayer_dense_wt_q7_cmsis	(	ailayer_dense_t *	layer,
		ailayer_t *	input_layer
	)

Initializes and connect a Dense layer with the Q7 AMR CMSIS implementation for transposed weights tensor.

The weights tensor has to be transposed for this implementation, like in ailayer_dense_wt_q7_default().

Example: Create the layer structure with pretrained weights:
In C:

// Use constant data only for inference. For training remove the const qualifier!!
// Weights (8 bit quantized)
const aimath_q7_params_t weights_q_params_dense = { .shift = 3, .zero_point = 0 };
const int8_t weights_data_dense[] = {-81, 58, -67, -61, 44, -72};
// Bias (32 bit quantized)
const aimath_q31_params_t bias_q_params_dense = { .shift = 10, .zero_point = 0 };
const int32_t bias_data_dense[] = {-3036,  2425, -1635};
// Result (8 bit quantized)
const aimath_q7_params_t result_q_params_dense = { .shift = 3, .zero_point = 41 };
ailayer_dense_q7_t dense_layer = {
    .neurons = 3,
    .weights = {
        .tensor_params = (aimath_q7_params_t *) &weights_q_params_dense,
        .data = (int8_t *) weights_data_dense
    },
    .bias = {
        .tensor_params = (aimath_q31_params_t *) &bias_q_params_dense,
        .data = (int32_t *) bias_data_dense
    },
    .base.result.tensor_params = (aimath_q7_params_t *) &result_q_params_dense
};

In C, C++ and on Arduino:

// Use constant data only for inference. For training remove the const qualifier!!
// Weights (8 bit quantized)
const aimath_q7_params_t weights_q_params_dense = { .shift = 3, .zero_point = 0 };
const int8_t weights_data_dense[] = {-81, 58, -67, -61, 44, -72};
// Bias (32 bit quantized)
const aimath_q31_params_t bias_q_params_dense = { .shift = 10, .zero_point = 0 };
const int32_t bias_data_dense[] = {-3036,  2425, -1635};
// Result (8 bit quantized)
const aimath_q7_params_t result_q_params_dense = { .shift = 3, .zero_point = 41 };
ailayer_dense_q7_t dense_layer = AILAYER_DENSE_Q7_M(3,
                                                    weights_data_dense, &weights_q_params_dense,
                                                    bias_data_dense, &bias_q_params_dense,
                                                    &result_q_params_dense);

Example: Create the layer structure for automatic parameter distribution:
In C:

ailayer_dense_q7_t dense_layer = {
    .neurons = 3
};

In C, C++ and on Arduino:

ailayer_dense_q7_t dense_layer = AILAYER_DENSE_Q7_A(3);

Example: Initialize and connect the layer:

x = ailayer_dense_wt_q7_cmsis(&dense_layer, x);

Parameters

*layer	The layer structure to initialize.
*input_layer	The prior layer.

Returns

The (successfully) initialized layer structure.