VLSI for Neural Networks and Artificial Intelligence 1994 edition

Marc Notes: Proceedings of an International Workshop on Artificial Intelligence and Neural Networks, held September 2-4, 1992, in Oxford, United Kingdom--T.p. verso.; Includes bibliographical references and index. Table of Contents: Analog Circuits for Neural Networks: Analog VLSI Neural Learning Circuits (H. C. Card). An Analog CMOS Implementation of a Kohonen Network with Learning Capability (O. Landolt). BackPropagation Learning Algorithms for Analog VLSI Implementation (M. Valle et al.). Digital Implementations of Neural Networks: A VLSI Pipelined Neuroemulator (J. G. DelgadoFrias et al.). A Low Latency Digital Neural Network Architecture (W. Fornaciari, F. Salice). The MANTRA Project (M. A. Viredaz et al.). Neural Networks on Multiprocessor Systems and Applications: VLSIImplementation of Associative Memory Systems for Neural Information Processing (A. Konig, M. Glesner). A Dataflow Approach for Neural Networks (J. G. DelgadoFrias et al.). Knowledge Processing in Neural Architechture (U. Ruckert et al.). VLSI Machines for Artificial Intelligence: Hardware Support for Data Parallelism in Production Systems (S. H. Lavington et al.). SPACE (K. Asanovic, D. B. Howe). 19 additional articles. Index."Publisher Marketing: 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.