Associative memories based on multiple-valued sparse clustered networks

Hooman Jarollahi, Naoya Onizawa, Takahiro Hanyu, Warren J. Gross

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Associative memories are structures that store data patterns and retrieve them given partial inputs. Sparse Clustered Networks (SCNs) are recently-introduced binary-weighted associative memories that significantly improve the storage and retrieval capabilities over the prior state-of-the art. However, deleting or updating the data patterns result in a significant increase in the data retrieval error probability. In this paper, we propose an algorithm to address this problem by incorporating multiple-valued weights for the interconnections used in the network. The proposed algorithm lowers the error rate by an order of magnitude for our sample network with 60% deleted contents. We then investigate the advantages of the proposed algorithm for hardware implementations.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 44th International Symposium on Multiple-Valued Logic, ISMVL 2014
PublisherIEEE Computer Society
Pages208-213
Number of pages6
ISBN (Print)9781479935345
DOIs
Publication statusPublished - 2014 Jan 1
Event44th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2014 - Bremen, Germany
Duration: 2014 May 192014 May 21

Publication series

NameProceedings of The International Symposium on Multiple-Valued Logic
ISSN (Print)0195-623X

Other

Other44th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2014
CountryGermany
CityBremen
Period14/5/1914/5/21

Keywords

  • Associative Memory
  • Sparse Clustered Networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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