Parallel algorithms/architectures for neural networks

J. N. Hwang, S. Y. Kung

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This paper advocates digital VLSI architectures for implementing a wide variety of artificial neural networks (ANNs). A programmable systolic array is proposed, which maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. The array is meant to be more general purpose than most other ANN architectures proposed. It may be used for a variety of algorithms in both the retrieving and learning phases of ANNs: e.g., single layer feedback networks, competitive learning networks, and multilayer feed-forward networks. A unified approach to modeling of existing neural networks is proposed. This unified formulation leads to a basic structure for a universal simulation tool and neurocomputer architecture. Fault-tolerance approach and partitioning scheme for large or non-homogeneous networks are also proposed. Finally, the implementations based on commercially available VLSI chips (e.g., Inmos T800) and custom VLSI technology are discussed in great detail.

Original languageEnglish (US)
Pages (from-to)221-251
Number of pages31
JournalJournal of VLSI Signal Processing
Volume1
Issue number3
DOIs
StatePublished - Sep 1 1989

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering

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