Description
This book covers all major aspects of state of the art research within 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) viewpoint on designing efficient bio-inspired hardware. On the nanodevice level, it specializes in more than a few flavors of emerging resistive memory (RRAM) technology. On the set of rules 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 call a couple of. The contributions talk about system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested within the field.