Inspired by the biological neural networks, artificial neural networks are able to solve complex problems, by performing a tremendous amount of relatively simple parallel computations. Embedding such networks in autonomous devices raises the issues of energy efficiency, resource usage and accuracy. This presentation surveys the efforts made in recent years to implement artificial neural network architectures in dedicated hardware devices to make them usable in embedded applications.

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