ESP-NN
The library contains optimised NN (Neural Network) functions for various Espressif chipsets.
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Supported platforms:
- TensorFlow Lite Micro (TFLite Micro). Repo can be found here
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Supported ESP chipsets include:
- ESP32-S3 (Assembly versions optimised to benefit from vector instructions of ESP32-S3)
- ESP32 (Generic optimisations)
- ESP32-C3 (Generic optimisations)
Performance
Kernelwise performance for s8 versions:
- Kernelwise performance on ESP32-S3 chip
- Numbers are ticks taken for kernel to execute
- Chip config: 240MHz, SPI: QPI 80MHz, Data cache: 64KB
Function ANSI C ESP32-S3 Opt Opt Ratio Data info Memory elementwise_add 320397 87119 3.68 size = 1615 External elementwise_mul 125958 44239 2.85 size = 1615 External convolution 4663012 428675 10.88 input(10,10), filter(64x1x1x64) External convolution 301014 32433 9.28 input(8,8), filter(16x1x1x16) External convolution 2115418 1020923 2.07 input(10,10), filter(64x3x3x3) External depthwise conv 1190062 203278 5.85 input (18, 18), pad(0,0), stride(1,1) filter: 1x3x3x16 External depthwise conv 837072 182335 4.59 input (12, 12), pad(1,1), stride(1,1) filter: 8x5x5x4 External max pool 485714 76747 6.33 input(16,16), filter (1x3x3x16) Internal avg pool 541462 160580 3.37 input(16,16), filter (1x3x3x16) Internal fully connected 15853 9547 1.66 len: 265, ch = 3 Internal prelu (relu6) 19472 2734 7.12 size, 1615 Internal
Configuration
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To configure, please use
idf.py menuconfig
and underESP-NN
selectNN_OPTIMIZATIONS
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There are two options presented:
- Optimized versions
- ANSI C
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Default selection is for
Optimized versions
. For ESP32-S3, assembly versions are automatically selected, whereas for other chipsets (viz., ESP32, ESP32-C3), generic optimisations are selected. -
For debugging purposes, you may want to select
ANSI C
reference versions.
Contributing
If you encounter an issue with ESP-NN, or wish to submit a feature request, please use the Issues section on the Github.
For general questions related to this library, please use the esp32.com forum.
Copyrights and License
All original source code in this repository is Copyright (C) 2020-2021 Espressif Systems. This source code is licensed under the Apache License 2.0 as described in the file LICENSE.