Table of contents
About this book
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.
• Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level;
• Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system;
• Focuses on new experimental results that are likely to solve practical, artificial intelligent problems, such as image classification.
neuro-inspired computing resistive synaptic devices Phase change memory peripheral neuron circuits design memristors