國立虎尾科技大學 |

Advances in neuromorphic hardware exploiting emerging nanoscale devices

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advances in neuromorphic hardware exploiting emerging nanoscale devices/ edited by Manan Suri.
其他作者:	Suri, Manan.
出版者:	New Delhi :Springer India : : 2017.,
面頁冊數:	xiii, 210 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-81-322-3703-7
ISBN:	9788132237037

Advances in neuromorphic hardware exploiting emerging nanoscale devices
Advances in neuromorphic hardware exploiting emerging nanoscale devices[electronic resource] /edited by Manan Suri. - New Delhi :Springer India :2017. - xiii, 210 p. :ill., digital ;24 cm. - Cognitive systems monographs,v.311867-4925 ;. - Cognitive systems monographs ;v.16..

Phase Change Memory for Neuromorphics -- Filamentary resistive memory for Neuromorphics -- Metal oxide based memory for Neuromorphics -- Nano Organic Transistors for Neuromorphics -- Neuromorphic System design -- Neuromorphic System and algorithms optimization -- Memristor Technology for Neuromorphics -- PCMO based devices for Neuromorphics -- Resistive Memory for Neuromorphics -- Overall Perspective on Neuromorphic Hardware.

This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.

ISBN: 9788132237037

Standard No.: 10.1007/978-81-322-3703-7doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Advances in neuromorphic hardware exploiting emerging nanoscale devices
LDR:02423nam a2200325 a 4500 001 958427
003 DE-He213
005 20170810170831.0
006 m d
007 cr nn 008maaau
008 201118s2017 ii s 0 eng d
020 $a 9788132237037 $q (electronic bk.)
020 $a 9788132237013 $q (paper)
024 7 $a 10.1007/978-81-322-3703-7 $2 doi
035 $a 978-81-322-3703-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .A244 2017
245 0 0 $a Advances in neuromorphic hardware exploiting emerging nanoscale devices $h [electronic resource] / $c edited by Manan Suri.
260 $a New Delhi : $c 2017. $b Springer India : $b Imprint: Springer,
300 $a xiii, 210 p. : $b ill., digital ; $c 24 cm.
490 1 $a Cognitive systems monographs, $x 1867-4925 ; $v v.31
505 0 $a Phase Change Memory for Neuromorphics -- Filamentary resistive memory for Neuromorphics -- Metal oxide based memory for Neuromorphics -- Nano Organic Transistors for Neuromorphics -- Neuromorphic System design -- Neuromorphic System and algorithms optimization -- Memristor Technology for Neuromorphics -- PCMO based devices for Neuromorphics -- Resistive Memory for Neuromorphics -- Overall Perspective on Neuromorphic Hardware.
520 $a This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Computer architecture. $3 528145
650 0 $a Analog CMOS integrated circuits. $3 1062812
650 1 4 $a Engineering. $3 561152
650 2 4 $a Circuits and Systems. $3 670901
650 2 4 $a User Interfaces and Human Computer Interaction. $3 669793
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Nanotechnology and Microengineering. $3 722030
700 1 $a Suri, Manan. $3 1250563
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Cognitive systems monographs ; $v v.16. $3 890014
856 4 0 $u http://dx.doi.org/10.1007/978-81-322-3703-7
950 $a Engineering (Springer-11647)