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Comment by snops | Hacker News Reader

I have previously seen Microsoft Embedded Learning Library[1] which requires LLVM support for the target arch IIRC, and uTensor[2] which runs TensorFlow models on larger ARM cortex micros.

Nice to see another group in Microsoft is targeting smaller 8/16 bit processors, they can still be useful for very limited power budget applications, low cost devices, and some odd applications where they are built in to a specific purpose IC (e.g. a flash drive controller). There aren't many other alternatives in this space I think, other than manually porting your model to C and running unit tests against it, does anyone know of any competitors?

[1]https://github.com/Microsoft/ELL [2]https://github.com/uTensor/uTensor

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Also interested in more competitors, as I'm researching and developing machinelearning for microcontrollers. Two libraries I have made are:

https://github.com/jonnor/emtrees https://github.com/jonnor/embayes

These are likely to be consolidated into one library/framework in some weeks, along with some other models and tools I have lying around. Like simple neural networks and audio feature extraction.

My brain dump with links can be found here, https://github.com/jonnor/datascience-master/blob/master/emb...

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

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...

sklearn-porter can be used to compile some scikit-learn models to C. For instance Support Vector Machines, both linear and RBF,polynomial kernels.

https://github.com/nok/sklearn-porter/blob/master/readme.md#...

ProtoNN is another classifier especially for low-resource systems. http://proceedings.mlr.press/v70/gupta17a.html

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