MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04885nam a22005655i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
TR-AnTOB |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231121102209.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220525s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031038419 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-3-031-03841-9 |
| Source of number or code |
doi |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
TR-AnTOB |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
TR-AnTOB |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
İngilizce |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TK7895.M4 |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
TJF |
| Source |
bicssc |
|
|---|
| Subject category code |
TEC008000 |
| Source |
bisacsh |
|
|---|
| Subject category code |
TJF |
| Source |
thema |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
TK7895.M4EBK |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine Learning and Non-volatile Memories |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc. |
edited by Rino Micheloni, Cristian Zambelli. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2022. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Cham : |
| Name of producer, publisher, distributor, manufacturer |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Electronics. |
|
|---|
| Topical term or geographic name entry element |
Artificial intelligence. |
|
|---|
| Topical term or geographic name entry element |
Computers. |
|
|---|
| Topical term or geographic name entry element |
Computer engineering. |
|
|---|
| Topical term or geographic name entry element |
Computer networks . |
|
|---|
| Topical term or geographic name entry element |
Electronics and Microelectronics, Instrumentation. |
|
|---|
| Topical term or geographic name entry element |
Artificial Intelligence. |
|
|---|
| Topical term or geographic name entry element |
Computer Hardware. |
|
|---|
| Topical term or geographic name entry element |
Computer Engineering and Networks. |
|
|---|
| Topical term or geographic name entry element |
Computer Hardware. |
| 653 #0 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Machine learning |
|
|---|
| Uncontrolled term |
Nonvolatile random-access memory |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Micheloni, Rino. |
| Relator term |
editor. |
| Relator code |
edt |
| -- |
http://id.loc.gov/vocabulary/relators/edt |
|
|---|
| Personal name |
Zambelli, Cristian. |
| Relator term |
editor. |
| Relator code |
edt |
| -- |
http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://doi.org/10.1007/978-3-031-03841-9">https://doi.org/10.1007/978-3-031-03841-9</a> |
| Materials specified |
Springer eBooks |
| Public note |
Online access link to the resource |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
E-Book |
