Machine Learning and Non-volatile Memories (eBook)

This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first 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 without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state drives (SSDs).

Dr. Rino Micheloni is a Research Fellow at the University of Ferrara, Italy. Before that, he was Vice-President and Fellow at Microsemi/Microchip Corporation, where he established the Flash Signal Processing Labs in Milan, Italy, with special focus on NAND Flash technology characterization, machine learning techniques for improving memory reliability, and error correction codes. Prior to joining Microsemi, he was Fellow at PMC-Sierra, working on NAND Flash technology characterization, LDPC, and NAND Signal Processing as part of the team developing flash controllers for PCIe SSDs. Before that, he was with Integrated Device Technology (IDT) as Lead Flash Technologist, driving the architecture and design of the BCH engine in the world's first PCIe NVMe SSD controller. Early in his career, he led NAND design teams at STMicroelectronics, Hynix, and Infineon; during this time, he developed the industry's first MLC NOR device with embedded ECC technology and the industry's first MLC NAND with embedded BCH.