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VLSI for Neural Networks and Artificial Intelligence - Jose G. Delgado-Frias

VLSI for Neural Networks and Artificial Intelligence

By: Jose G. Delgado-Frias (Editor), W.R. Moore (Editor)

Hardcover Published: 30th September 1994
ISBN: 9780306447228
Number Of Pages: 320

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Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.

Analog VLSI Neural Learning Circuits - A Tutorialp. 1
An Analog CMOS Implementation of a Kohonen Network with Learning Capabilityp. 25
Back-Propagation Learning Algorithms for Analog VLSI Implementationp. 35
An Analog Implementation of the Boltzmann Machine with Programmable Learning Algorithmsp. 45
A VLSI Design of the Minimum Entropy Neuronp. 53
A Multi-Layer Analog VLSI Architecture for Texture Analysis Isomorphic to Cortical Cells in Mammalian Visual Systemp. 61
A VLSI Pipelined Neuroemulatorp. 71
A Low Latency Digital Neural Network Architecturep. 81
MANTRA: A Multi-Model Neural-Network Computerp. 93
SPERT: A Neuro-Microprocessorp. 103
Design of Neural Self-Organization Chips for Semantic Applicationsp. 109
VLSI Implementation of a Digital Neural Network with Reward-Penalty Learningp. 119
Asynchronous VLSI Design for Neural System Implementationp. 129
VLSI-Implementation of Associative Memory Systems for Neural Information Processingp. 141
A Dataflow Approach for Neural Networksp. 151
A Custom Associative Chip Used as a Building Block for a Software Reconfigurable Multi-Network Simulatorp. 159
Parallel Implementation of Neural Associative Memories on RISC Processorsp. 167
Reconfigurable Logic Implementation of Memory-Based Neural Networks: A Case Study of the CMAC Networkp. 177
A Cascadable VLSI Design for GENETp. 187
Parametrised Neural Network Design and Compilation into Hardwarep. 197
Knowledge Processing in Neural Architecturep. 207
Two Methods for Solving Linear Equations Using Neural Networksp. 217
Hardware Support for Data Parallelism in Production Systemsp. 231
SPACE: Symbolic Processing in Associative Computing Elementsp. 243
PALM: A Logic Programming System on a Highly Parallel Architecturep. 253
A Distributed Parallel Associative Processor (DPAP) for the Execution of Logic Programsp. 265
Performance Analysis of a Parallel VLSI Architecture for Prologp. 275
A Prolog VLSI System for Real Time Applicationsp. 285
An Extended WAM Based Architecture for OR-Parallel Prolog Executionp. 297
Architecture and VLSI Implementation of a Pegasus-II Prolog Processorp. 307
Contributorsp. 317
Indexp. 319
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780306447228
ISBN-10: 0306447223
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 320
Published: 30th September 1994
Publisher: Springer Science+Business Media
Country of Publication: US
Dimensions (cm): 23.4 x 15.6  x 1.91
Weight (kg): 1.43