VLSI-compatible Implementations for Artificial Neural Networks

Fakhraie, Sied Mehdi; Smith, K. C. A.

ISBN 10: 0792398254 / ISBN 13: 9780792398257

Published by Kluwer Academic Publishers, 1996

About this Item

Introduces the basic premise of the authors' approach to biologically-inspired and VLSI-compatible definition, simulation, and implementation of artificial neural networks. This book develops a set of guidelines for general hardware implementation of ANNs. It may also be used as a text for advanced courses on the subject. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 223 pages, biography. BIC Classification: TJF; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 14. Weight in Grams: 498. . 1996. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780792398257

Bibliographic Details

• Title: VLSI-compatible Implementations for Artificial Neural Networks
• Publisher: Kluwer Academic Publishers
• Publication Date: 1996
• Language: English
• ISBN 10: 0792398254
• ISBN 13: 9780792398257
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This book introduces several state-of-the-art VLSI implementations of artificial neural networks (ANNs). It reviews various hardware approaches to ANN implementations: analog, digital and pulse-coded. The analog approach is emphasized as the main one taken in the later chapters of the book. The area of VLSI implementation of ANNs has been progressing for the last 15 years, but not at the fast pace originally predicted. Several reasons have contributed to the slow progress, with the main one being that VLSI implementation of ANNs is an interdisciplinaly area where only a few researchers, academics and graduate students are willing to venture. The work of Professors Fakhraie and Smith, presented in this book, is a welcome addition to the state-of-the-art and will greatly benefit researchers and students working in this area. Of particular value is the use of experimental results to backup extensive simulations and in-depth modeling. The introduction of a synapse-MOS device is novel. The book applies the concept to a number of applications and guides the reader through more possible applications for future work. I am confident that the book will benefit a potentially wide readership. M. I. Elmasry University of Waterloo Waterloo, Ontario Canada Preface Neural Networks (NNs), generally defined as parallel networks that employ a large number of simple processing elements to perform computation in a distributed fashion, have attracted a lot of attention in the past fifty years. As the result. many new discoveries have been made.