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Books > Computing & IT > Computer communications & networking
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Machine Learning and Non-volatile Memories (Hardcover, 1st ed. 2022)
Loot Price: R4,233
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Machine Learning and Non-volatile Memories (Hardcover, 1st ed. 2022)
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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.
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