AI-on-the-edge-device/code/components/esp-nn

ESP-NN

The library contains optimised NN (Neural Network) functions for various Espressif chipsets.

• Supported platforms:

  • TensorFlow Lite Micro (TFLite Micro). Repo can be found here

• Supported ESP chipsets include:

  • ESP32-S3 (Assembly versions optimised to benefit from vector instructions of ESP32-S3)
  • ESP32 (ANSI C versions)

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

• 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 ESP32, ANSI-C versions are selected by default.

• For debugging purposes, you may want to select ANSI C

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.