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Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity Recognition. However, there is still room for optimization of deep neural networks onto embedded devices. These optimizations mainly address power consumption, memory and real-time constraints, but also an easier deployment at the edge. Moreover, there is still a need for a better understanding of what can be achieved for different use cases. This work focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. The quantization methods, relevant in the context of an embedded execution onto a microcontroller, are first outlined. Then, a new framework for end-to-end deep neural networks training, quantization and deploymen

Ambiq apolloQuantization of deep neural networksDeployment of quantized neural networkNeural networkNeural networksHuman activity recognitionFloating point unitTensorflow liteSparkfun edgeEmbedded executionQuantized neuralStatistics aware weight binningLearned step size quantizationDifferentiable quantizationDeep neural networksGaussian mixture models