/esp-nn

Optimised Neural Network functions for Espressif chipsets

Primary LanguageAssemblyApache License 2.0Apache-2.0

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

  • 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-P4 chip

    • Numbers are ticks taken for kernel to execute
    • Chip config: 360MHz, SPI-RAM: HEX 200MHz, L2-Cache: 128KB
    Function ANSI C Optimized Opt Ratio Data info Memory
    elementwise_add 187971 173104 -- size = 1615 External
    elementwise_mul 79898 71245 -- size = 1615 External
    convolution 4005512 572459 7.00 input(10,10), filter(64x1x1x64), pad(0,0), stride(1,1) External
    convolution 249389 98319 2.54 input(8,8), filter(16x1x1x16), pad(0,0), stride(1,1) External
    convolution 816975 533318 1.53 input(10,10), filter(64x3x3x3), pad(0,0), stride(1,1) External
    depthwise conv 962834 482389 2.00 input (16, 16), pad(0,0), stride(1,1) filter: 1x3x3x16 External
    depthwise conv 1365066 703989 1.94 input (12, 12), pad(1,1), stride(1,1) filter: 8x5x5x4 External
    max pool 601843 592189 -- input(16,16), filter (1x3x3x16) Internal
    avg pool 392947 380527 -- input(16,16), filter (1x3x3x16) Internal
    fully connected 7692 7616 -- len: 271, ch = 3 Internal
    prelu (relu6) 22487 18963 -- size, 1615 Internal

  • 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 and ESP32-P4, 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 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.