VLSI for Neural Networks and Artificial Intelligence
Published on 30. September 1994
Book
Hardback
X, 320 pages
978-0-306-44722-8 (ISBN)
Description
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.
More details
Edition
1994 ed.
Language
English
Place of publication
New York
United States
Publishing group
Springer Science+Business Media
Target group
College/higher education
Professional and scholarly
Research
Illustrations
X, 320 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 23 mm
Weight
670 gr
ISBN-13
978-0-306-44722-8 (9780306447228)
DOI
10.1007/978-1-4899-1331-9
Schweitzer Classification
Other editions
Additional editions
Jose G. Delgado-Frias | W.R. Moore
VLSI for Neural Networks and Artificial Intelligence
E-Book
06/2013
Springer
€149.79
Available for download
Jose G. Delgado-Frias | W.R. Moore
VLSI for Neural Networks and Artificial Intelligence
Book
06/2013
Springer
€160.49
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Content
Analog Circuits for Neural Networks.- Analog VLSI Neural Learning Circuits -A Tutorial.- An Analog CMOS Implementation of a Kohonen Network with Learning Capability.- Back-Propagation Learning Algorithms for Analog VLSI Implementation.- An Analog Implementation of the Boltzmann Machine with Programmable Learning Algorithms.- A VLSI Design of the Minimum Entropy Neuron.- A Multi-Layer Analog VLSI Architecture for Texture Analysis Isomorphic to Cortical Cells in Mammalian Visual System.- Digital Implementations of Neural Networks.- A VLSI Pipelined Neuroemulator.- A Low Latency Digital Neural Network Architecture.- MANTRA: A Multi-Model Neural-Network Computer.- SPERT: A Neuro-Microprocessor.- Design of Neural Self-Organization Chips for Semantic Applications.- VLSI Implementation of a Digital Neural Network with Reward-Penalty Learning.- Asynchronous VLSI Design for Neural System Implementation.- Neural Networks on Multiprocessor Systems and Applications.- VLSI-Implementation of Associative Memory Systems for Neural Information Processing.- A Dataflow Approach for Neural Networks.- A Custom Associative Chip Used as a Building Block for a Software Reconfigurable Multi-Network Simulator.- Parallel Implementation of Neural Associative Memories on RISC Processors.- Reconfigurable Logic Implementation of Memory-Based Neural Networks: A Case Study of the CMAC Network.- A Cascadable VLSI Design for GENET.- Parametrised Neural Network Design and Compilation into Hardware.- Knowledge Processing in Neural Architecture.- Two Methods for Solving Linear Equations Using Neural Networks.- VLSI Machines for Artificial Intelligence.- Hardware Support for Data Parallelism in Production Systems.- SPACE: Symbolic Processing in Associative Computing Elements.- PALM: A Logic Programming System on a Highly Parallel Architecture.- A Distributed Parallel Associative Processor (DPAP) for the Execution of Logic Programs.- Performance Analysis of a Parallel VLSI Architecture for Prolog.- A Prolog VLSI System for Real Time Applications.- An Extended WAM Based Architecture for OR-Parallel Prolog Execution.- Architecture and VLSI Implementation of a Pegasus-II Prolog Processor.- Contributors.