Titlebook: Machine Learning and Non-volatile Memories; Rino Micheloni,Cristian Zambelli Book 2022 The Editor(s) (if applicable) and The Author(s), un

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wer hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardwar978-3-031-03843-3978-3-031-03841-9

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,Accelerating Deep Neural Networks with?Phase-Change Memory Devices,nference using PCM?arrays. We present a technique to compensate for conductance drift (“slope correction”) to allow in-memory computing with PCM?during inference to reach software-equivalent deep learning baselines for a broad variety of important neural network workloads.

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Book 2022 sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even w

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,Neural Networks and?Deep Learning Fundamentals,ter vision to speech-to-text, DL?techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.