On partitioning and fault tolerance issues for neural array processors

Karl heinz Zimmermann, Tien chien Lee, Sun yuan Kung

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

In this article, we have studied time-efficient schedule and fault-tolerant design of partitioned array processors for neural networks. First, we have applied the locally-sequential-globally-parallel (LSGP) partitioning scheme to decompose large-size neural network algorithms so that they can be mapped into array processors of smaller size. Then we have derived an optimal latency schedule, i.e., for the same decomposition the schedule outperforms any other schedule, in terms of overall execution time. We have further proposed an algorithm-based fault tolerance (ABFT) method to guarantee higher reliability for the array processor implementation.

Original languageEnglish (US)
Pages (from-to)85-94
Number of pages10
JournalJournal of VLSI Signal Processing
Volume6
Issue number1
DOIs
StatePublished - Jun 1 1993

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering

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