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.
