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I've been working on a deep learning library in C, and have been thinking about optimizing it for embedded applications specifically.

https://github.com/siekmanj/sieknet

Other than that, I'm not sure if there are many libraries for machine learning on microcontrollers. Genann comes to mind:

https://github.com/codeplea/genann

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Nice. What are you thinking for optimizing for embedded use? In my opinion the main challenge for neural networks on microcontrollers is the amount of memory needed for weights.

- Quantizing the weights to lower precision is an easy gain. CMSISNN (which uTensor will use on Cortex-Mx) uses 8 bit fixed-point.

- Utilizing sparse weights from regularization (L1,L0) may also give some gains.

But apart from these I think more innovative things will be needed?

Looks like state of the art can achieve up to 120x compression on CNNs for image classification. https://arxiv.org/abs/1802.02271

I used quantization of weights and an adaptation of the training, more information on the link above:

https://www.researchgate.net/publication/304424659_Performan...

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