Overview
- Editors:
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Mohamed I. Elmasry
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VLSI Research Group, University of Waterloo, Canada
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Table of contents (9 chapters)
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- Waleed Fakhr, Mohamed I. Elmasry
Pages 1-31
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- Sameh E. Rehan, Mohamed I. Elmasry
Pages 33-89
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- Arun Achyuthan, Mohamed I. Elmasry
Pages 91-137
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- Liang-Yong Song, Anthony Vannelli, Mohamed I. Elmasry
Pages 139-156
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- Brian White, Mohamed I. Elmasry
Pages 157-189
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- Khaled Hassanein, Li Deng, Mohamed I. Elmasry
Pages 191-245
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- Waleed Fakhr, Mohamed Kamel, Mohamed I. Elmasry
Pages 247-282
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- Dapeng Zhang, Mohamed Kamel, Mohamed I. Elmasry
Pages 283-302
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- Dapeng Zhang, Li Deng, Mohamed I. Elmasry
Pages 303-321
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Back Matter
Pages 323-329
About this book
Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980's. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys tems", Addison-Wesley, 1989; there were a number of excellent text and ref erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.
Editors and Affiliations
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VLSI Research Group, University of Waterloo, Canada
Mohamed I. Elmasry
