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Advances in neuromorphic hardware ex...

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Advances in neuromorphic hardware exploiting emerging nanoscale devices

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in neuromorphic hardware exploiting emerging nanoscale devicesedited 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$q(electronic bk.)

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$q(electronic bk.)

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

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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