Advances in neuromorphic hardware exploiting emerging nanoscale devices
- 其他作者：
- 其他題名：
- Cognitive systems monographs ;
- 出版： New Delhi : Springer India :Imprint: Springer
- 叢書名： Cognitive systems monographs,volume 31
- 主題： Neural networks (Computer science) , Computer architecture. , Analog CMOS integrated circuits. , Engineering. , Circuits and Systems. , User Interfaces and Human Computer Interaction. , Computational Intelligence. , Nanotechnology and Microengineering.
- ISBN： 9788132237037 (electronic bk.) 、 9788132237013 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 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.
- 摘要註: 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.
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讀者標籤：
- 系統號： 005382672 | 機讀編目格式
館藏資訊
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.