espressif/esp-nn

uploaded 1 month ago

# ESP-NN The library contains optimised NN (Neural Network) functions for various Espressif chips. * Supported platforms: * TensorFlow Lite Micro (TFLite Micro). Repo can be found [here](https://github.com/espressif/tflite-micro-esp-examples) * Supported ESP chips 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 | Optimized | Opt Ratio | Data info | Memory | | ----------------| --------|---------|---------|-------------|-----------| | elementwise_add | 312327 | 71644 | 4.36 | size = 1615 | External | | elementwise_mul | 122046 | 30950 | 3.95 | size = 1615 | External | | convolution | 4642259 | 461398 | 10.06 | input(10,10), filter(64x1x1x64), pad(0,0), stride(1,1) | External | | convolution | 300032 | 43578 | 6.9 | input(8,8), filter(16x1x1x16), pad(0,0), stride(1,1) | External | | convolution | 2106801 | 643689 | 3.27 | input(10,10), filter(64x3x3x3), pad(0,0), stride(1,1) | External | | depthwise conv | 1192832 | 191931 | 6.2 | input (18, 18), pad(0,0), stride(1,1) filter: 1x3x3x16 | External | | depthwise conv | 1679406 | 366102 | 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 | 12290 | 4439 | 2.77 | len: 265, ch = 3 | Internal | | prelu (relu6) | 18315 | 1856 | 9.87 | size, 1615 | Internal | ## Configuration * To configure, please use `idf.py menuconfig` and under `ESP-NN` select `NN_OPTIMIZATIONS` * There are two options presented: * Optimized versions * ANSI C * Default selection is for `Optimized versions`. For ESP32-S3, assembly versions are automatically selected, whereas for other chips (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. Please check [CONTRIBUTING.md](CONTRIBUTING.md) for further information if you'd like to contribute to ESP-NN. ## 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.