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)

• 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

• 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.

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.

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.