This book covers all major aspects of cuttingedge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid lowpower CMOSNanodevice design. The book offers readers a bidirectional (topdown and bottomup) perspective on designing efficient bioinspired 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 shastic learning paradigms such as: spiketimedependent plasticity (STDP), longterm potentiation (LTP), longterm depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss systemlevel power/energy/parasitic tradeoffs, and complex realworld applications. The book is suited for both advanced researchers and students interested in the field.
This book covers all major aspects of cuttingedge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid lowpower CMOSNanodevice design. The book offers readers a bidirectional (topdown and bottomup) perspective on designing efficient bioinspired 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 shastic learning paradigms such as: spiketimedependent plasticity (STDP), longterm potentiation (LTP), longterm depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss systemlevel power/energy/parasitic tradeoffs, and complex realworld applications. The book is suited for both advanced researchers and students interested in the field.
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