Machine learning and non-volatile memories / Rino Micheloni, Cristian Zambelli, editors.
Micheloni, Rino, editor.; Zambelli, Cristian, editor.
2022
Q325.5
Available Online

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Title Machine learning and non-volatile memories / Rino Micheloni, Cristian Zambelli, editors.
ISBN 9783031038419 (electronic bk.)
303103841X (electronic bk.)
3031038401
9783031038402
DOI https://doi.org/10.1007/978-3-031-03841-9
Publication Details Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource
Item Number 10.1007/978-3-031-03841-9 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary 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). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power 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 hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called neuromorphic architecture, which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 10, 2022).
Added Author Micheloni, Rino, editor.
Zambelli, Cristian, editor.
Available in Other Form Print version: 9783031038402
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All Resources
Introduction to Machine Learning
Neural Networks and Deep Learning Fundamentals
Accelerating Deep Neural Networks with Analog Memory Devices
Analog In-memory Computing with Resistive Switching Memories
Introduction to 3D NAND Flash Memories.

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