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Springer VLSI for Neural Networks and Artificial Intelligence - Springer

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Description

This comprehensive Springer text examines the intersection of VLSI design and advanced computing for neural networks and artificial intelligence systems. As neural network algorithms and AI applications have grown increasingly complex, the computational demands have exceeded what conventional scalar processors can efficiently deliver. This book addresses the unique constraints and requirements of neurocomputing systems, including non-programming capabilities and learning functions, alongside AI demands such as symbolic manipulation and knowledge representation. It provides essential insights into hardware architecture and microprocessor design specifically tailored for neural network implementations and intelligent systems, offering both theoretical foundations and practical approaches to building efficient AI-capable hardware solutions.

Key Features

Springer publication covering VLSI design principles for neural network and artificial intelligence hardware implementations.

Explores computational architecture requirements that extend beyond conventional scalar processor capabilities for complex AI algorithms.

Addresses neurocomputing constraints including non-programming and learning requirements specific to neural network systems.

Examines artificial intelligence hardware demands such as symbolic manipulation and knowledge representation in VLSI design.

Provides guidance on designing microprocessors and computer architecture optimized for neural network and AI applications.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 September 1994
Listed Since
12 January 2007

Barcode

No barcode data available

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