Description: VLSI - Compatible Implementations for Artificial Neural Networks Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Sied Mehdi Fakhraie, Kenneth C. Smith Format: Paperback Publisher: Springer-Verlag New York Inc., United States Imprint: Springer-Verlag New York Inc. ISBN-13: 9781461378976, 978-1461378976 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.
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Book Title: VLSI - Compatible Implementations for Artificial Neural Networks
Number of Pages: 194 Pages
Publication Name: Vlsi-Compatible Implementations for Artificial Neural Networks
Language: English
Publisher: Springer-Verlag New York Inc.
Item Height: 235 mm
Subject: Physics
Publication Year: 2012
Type: Textbook
Item Weight: 355 g
Subject Area: Electrical Engineering
Author: Kenneth C. Smith, Sied Mehdi Fakhraie
Item Width: 155 mm
Series: The Springer International Series in Engineering and Computer Science
Format: Paperback
