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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,Neural Networks and?Deep Learning Fundamentals,eep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer 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 comprehe

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Analogue In-Memory Computing with Resistive Switching Memories,mputing systems. In this scenario, in-memory computing is gaining momentum as a new methodology to overcome the von Neumann architecture and the related memory bottleneck. One of the most promising device for in-memory computation is the resistive switching memory (RRAM), also known as memristor, th

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Machine Learning for 3D NAND Flash and Solid State Drives Reliability/Performance Optimization, IEEE 105:1589–1608, 2017). As such, the performances and reliability of SSDs should be improved to reduce the Total Cost of Ownership of the system hosting the drive. This activity requires a careful evaluation of those figures exposed by the integrated storage medium, namely the 3D NAND Flash memo

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https://doi.org/10.1007/978-3-031-03841-9Artificial Intelligence; 3D NAND Flash Memory; Autonomous Solid-state-drive; NAND-specific Neural Netwo