VLSI Artificial Neural Networks Engineering

1. Front Matter

   Pages i-xv

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2. An Overview

   • Waleed Fakhr, Mohamed I. Elmasry

   Pages 1-31

3. A Sampled-Data CMOS VLSI Implementation of a Multi-Character ANN Recognition System

   • Sameh E. Rehan, Mohamed I. Elmasry

   Pages 33-89

4. A Design Automation Environment for Mixed Analog/Digital ANNs

   • Arun Achyuthan, Mohamed I. Elmasry

   Pages 91-137

5. A Compact VLSI Implementation of Neural Networks

   • Liang-Yong Song, Anthony Vannelli, Mohamed I. Elmasry

   Pages 139-156

6. An All-Digital VLSI ANN

   • Brian White, Mohamed I. Elmasry

   Pages 157-189

7. A Neural Predictive Hidden Markov Model Architecture for Speech and Speaker Recognition

   • Khaled Hassanein, Li Deng, Mohamed I. Elmasry

   Pages 191-245

8. Minimum Complexity Neural Networks for Classification

   • Waleed Fakhr, Mohamed Kamel, Mohamed I. Elmasry

   Pages 247-282

9. A Parallel ANN Architecture for Fuzzy Clustering

   • Dapeng Zhang, Mohamed Kamel, Mohamed I. Elmasry

   Pages 283-302

10. A Pipelined ANN Architecture for Speech Recognition

    • Dapeng Zhang, Li Deng, Mohamed I. Elmasry

    Pages 303-321

11. Back Matter

    Pages 323-329

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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.

• CMOS
• VLSI
• analog
• automation
• complexity
• design automation
• model
• network
• networks
• neural networks
• pattern recognition
• speech recognition

• VLSI Research Group, University of Waterloo, Canada

  Mohamed I. Elmasry

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