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Advances in neuromorphic hardware ex...

Suri, Manan.

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Advances in neuromorphic hardware exploiting emerging nanoscale devices

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in neuromorphic hardware exploiting emerging nanoscale devices/ edited by Manan Suri.
other author:	Suri, Manan.
Published:	New Delhi :Springer India : : 2017.,
Description:	xiii, 210 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science) -
Online resource:	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.31..

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:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Advances in neuromorphic hardware exploiting emerging nanoscale devices
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245 0 0 $a Advances in neuromorphic hardware exploiting emerging nanoscale devices $h [electronic resource] / $c edited by Manan Suri.
260 $a New Delhi : $b Springer India : $b Imprint: Springer, $c 2017.
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 532070
650 0 $a Computer architecture. $3 559837
650 0 $a Analog CMOS integrated circuits. $3 2133007
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650 2 4 $a User Interfaces and Human Computer Interaction. $3 892554
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Nanotechnology and Microengineering. $3 1005737
700 1 $a Suri, Manan. $3 3220618
710 2 $a SpringerLink (Online service) $3 836513
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950 $a Engineering (Springer-11647)