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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

• 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.