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 (Generic optimisations)
- ESP32-C3 (Generic optimisations)

Performance

Kernelwise performance on ESP32-S3 chip

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

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 chipsets (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.