espressif/esp-nn

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# 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-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](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.