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Jump, 1992 - Google Patents

A dynamically reconfigurable, M-SIMD, modular ring architecture for large-scale ANN implementations

Jump, 1992

Document ID
18161742872196280762
Author
Jump L
Publication year

External Links

Snippet

Abstract Artificial Neural Networks (ANNs) have been studied for almost half of a century now. ANN models have evolved from performing simple logic functions to finding self- adaptive solutions to complex recognition tasks. The 1980s saw an explosion of ANN …
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Classifications

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    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
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    • G06F15/76—Architectures of general purpose stored programme computers
    • G06F15/80—Architectures of general purpose stored programme computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
    • G06F15/8007—Architectures of general purpose stored programme computers comprising an array of processing units with common control, e.g. single instruction multiple data processors single instruction multiple data [SIMD] multiprocessors
    • G06F15/8023—Two dimensional arrays, e.g. mesh, torus
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    • G06F15/00—Digital computers in general; Data processing equipment in general
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    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
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