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 on ESP32-S3 chip

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.