Neuro-inspired Computing Using Resistive Synaptic Devices

This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices.The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network.Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration.This book also discusses peripheral neuron circuits design challenges and design strategies.Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.