大葉大學圖書館 |

Non-volatile in-memory computing by spintronics /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	621.3
書名/作者:	Non-volatile in-memory computing by spintronics // Hao Yu, Leibin Ni, Yuhao Wang
作者:	Yu, Hao
其他作者:	Ni, Leibin,
面頁冊數:	1 online resource (xiii, 147 pages) : : illustrations (some color)
標題:	Spintronics - Handbooks, manuals, etc.
標題:	Nonvolatile random-access memory
ISBN:	9781627056441
ISBN:	1627056440
書目註:	Includes bibliographical references and index
內容註:	1. Introduction -- 1.1 Memory wall -- 1.2 Traditional semiconductor memory -- 1.2.1 Overview -- 1.2.2 Nano-scale limitations -- 1.3 Non-volatile spintronic memory -- 1.3.1 Basic magnetization process -- 1.3.2 Magnetization damping -- 1.3.3 Spin-transfer torque -- 1.3.4 Magnetization dynamics -- 1.3.5 Domain wall propagation -- 1.4 Traditional memory architecture -- 1.5 Non-volatile in-memory computing architecture -- 1.6 References --
摘要、提要註:	Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book
電子資源:	http://portal.igpublish.com/iglibrary/search/MCPB0006290.html

Non-volatile in-memory computing by spintronics /
Yu, Hao(Electrical engineer),

Non-volatile in-memory computing by spintronics /Hao Yu, Leibin Ni, Yuhao Wang - 1 online resource (xiii, 147 pages) :illustrations (some color) - Synthesis lectures on emerging engineering technologies,2381-. - Synthesis lectures on emerging engineering technologies ;#8. .

Includes bibliographical references and index

1. Introduction -- 1.1 Memory wall -- 1.2 Traditional semiconductor memory -- 1.2.1 Overview -- 1.2.2 Nano-scale limitations -- 1.3 Non-volatile spintronic memory -- 1.3.1 Basic magnetization process -- 1.3.2 Magnetization damping -- 1.3.3 Spin-transfer torque -- 1.3.4 Magnetization dynamics -- 1.3.5 Domain wall propagation -- 1.4 Traditional memory architecture -- 1.5 Non-volatile in-memory computing architecture -- 1.6 References --

Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book

ISBN: 9781627056441

Standard No.: 10.2200 / S00736ED1V01Y201609EET008doiSubjects--Topical Terms:

653627
Spintronics
--Handbooks, manuals, etc.Subjects--Index Terms:

SpintronicsIndex Terms--Genre/Form:

344515
Electronic books

LC Class. No.: TK7874.887 / .Y85 2017

Dewey Class. No.: 621.3

Non-volatile in-memory computing by spintronics /
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505 0 $a 1. Introduction -- 1.1 Memory wall -- 1.2 Traditional semiconductor memory -- 1.2.1 Overview -- 1.2.2 Nano-scale limitations -- 1.3 Non-volatile spintronic memory -- 1.3.1 Basic magnetization process -- 1.3.2 Magnetization damping -- 1.3.3 Spin-transfer torque -- 1.3.4 Magnetization dynamics -- 1.3.5 Domain wall propagation -- 1.4 Traditional memory architecture -- 1.5 Non-volatile in-memory computing architecture -- 1.6 References --
505 8 $a 2. Non-volatile spintronic device and circuit -- 2.1 SPICE formulation with new nano-scale NVM devices -- 2.1.1 Traditional modified nodal analysis -- 2.1.2 New MNA with non-volatile state variables -- 2.2 STT-MTJ device and model -- 2.2.1 STT-MTJ -- 2.2.2 STT-RAM -- 2.2.3 Topological insulator -- 2.3 Domain wall device and model -- 2.3.1 Magnetization reversal -- 2.3.2 MTJ resistance -- 2.3.3 Domain wall propagation -- 2.3.4 Circular domain wall nanowire -- 2.4 Spintronic storage -- 2.4.1 Spintronic memory -- 2.4.2 Spintronic readout -- 2.5 Spintronic logic -- 2.5.1 XOR -- 2.5.2 Adder -- 2.5.3 Multiplier -- 2.5.4 LUT -- 2.6 Spintronic interconnect -- 2.6.1 Coding-based interconnect -- 2.6.2 Domain wall-based encoder/decoder -- 2.6.3 Performance evaluation -- 2.7 References --
505 8 $a 3. In-memory data encryption -- 3.1 In-memory advanced encryption standard -- 3.1.1 Fundamental of AES -- 3.1.2 Domain wall nanowire-based AES computing -- 3.1.3 Pipelined AES by domain wall nanowire -- 3.1.4 Performance evaluation -- 3.2 Domain wall-based SIMON block cipher -- 3.2.1 Fundamental of SIMON block cipher -- 3.2.2 Hardware stages -- 3.2.3 Round counter -- 3.2.4 Control signals -- 3.2.5 Key expansion -- 3.2.6 Encryption -- 3.2.7 Performance evaluation -- 3.3 References --
505 8 $a 4. In-memory data analytics -- 4.1 In-memory machine learning - - 4.1.1 Extreme learning machine -- 4.1.2 MapReduce-based matrix multiplication -- 4.1.3 Domain wall-based hardware mapping -- 4.1.4 Performance evaluation -- 4.2 In-memory face recognition -- 4.2.1 Energy-efficient STT-MRAM with Spare-represented data -- 4.2.2 QoS-aware adaptive current scaling -- 4.2.3 STT-RAM based hardware mapping -- 4.2.4 Performance evaluation -- 4.3 References -- Authors' biographies
520 3 $a Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book
588 $a Online resource; title from PDF title page (Morgan & Claypool, viewed on December 5, 2016)
650 0 $a Spintronics $v Handbooks, manuals, etc. $3 653627
650 0 $a Nonvolatile random-access memory $3 713964
653 $a Spintronics
653 $a in-memory computing
653 $a non-volatile memory
653 $a logic-memory integration
653 $a data encryption
653 $a AES
653 $a machine learning
653 $a data analytics
653 $a hardware accelerator
655 4 $a Electronic books $2 local. $3 344515
700 1 $a Ni, Leibin, $e author $3 713961
700 1 $a Wang, Yuhao $c (Electrical engineer), $e author $3 713962
776 0 8 $i Print version: $z 9781627052948
830 0 $a Synthesis lectures on emerging engineering technologies ; $v #8. $x 2381-1412 $3 713963
856 4 0 $u http://portal.igpublish.com/iglibrary/search/MCPB0006290.html