Publisher Description

This book approaches VLSI neural networks from a practical viewpoint, using case studies to show the full process of VLSI implementation of a network, and addressing the important issues of learning algorithms and limited precision effects. System aspects and low-power implementation issue are also covered. The authors are all international figures in the field from AT&T Bell Labs, Bellco and SEDAL. This book should be of interest to researchers and graduate students in neural networks VLSI and computer architecture.

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Back Cover

Adaptive Analog VLSI Neural Systems is the first practical book on neural networks learning chips and systems. It covers the entire process of implementing neural networks in VLSI chips, beginning with the crucial issues of learning algorithms in an analog framework and limited precision effects, and giving actual case studies of working systems. The approach is systems and applications oriented throughout, demonstrating the attractiveness of such an approach for applications such as adaptive pattern recognition and optical character recognition. Dr Jabri and his co-authors from AT&T Bell Laboratories, Bellcore and the University of Sydney provide a comprehensive introduction to VLSI neural networks suitable for research and development staff and advanced students.

Table of Contents

Overview. Introduction to neural computing. MOS devices and circuits. Analogue VLSI building blocks. Kakadu - a micropower neural network. Supervised learning in an analog framework. A micropower intracardiac electrogram classifier. On-chip perturbation based learning. An analog memory technique. Switched capacitor techniques. A high speed image understanding system. A Boltzmann machine learning system. References. Index.

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