Rino Micheloni, Cristian Zambelli Machine Learning and Non-volatile Memories 9783031038402

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Автор: Rino Micheloni, Cristian Zambelli (Микелони)
Название: Machine Learning and Non-volatile Memories
Перевод названия: Машинное обучение и энергонезависимая память
ISBN: 9783031038402
Издательство: Springer
Классификация:

Электроника

Накопители и периферийные устройства

Компьютерные сети и коммуникации

Машинное обучение

ISBN-10: 3031038401
Обложка/Формат: Hardback
Страницы: 161
Вес: 0.45 кг.
Дата издания: 09.06.2022
Серия: Synthesis lectures on technology and health
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 100 tables, color; 100 illustrations, color; 16 illustrations, black and white; xxiii, 161 p. 116 illus., 100 illus. in color.; 100 tables, color; 100
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Подзаголовок: Proceedings of the 12th and of the 13th international workshop on parallel tools for high performance computing, stuttgart, germany, september 2018, and dresden, germany, september 2019
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.

Machine Learning and Non-volatile Memories, Rino Micheloni, Cristian Zambelli