Selective decoding in associative memories based on Sparse-Clustered Networks

Hooman Jarollahi, Naoya Onizawa, Warren J. Gross

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Associative memories are structures that can retrieve previously stored information given a partial input pattern instead of an explicit address as in indexed memories. A few hardware approaches have recently been introduced for a new family of associative memories based on Sparse-Clustered Networks (SCN) that show attractive features. These architectures are suitable for implementations with low retrieval latency, but are limited to small networks that store a few hundred data entries. In this paper, a new hardware architecture of SCNs is proposed that features a new data-storage technique as well as a method we refer to as Selective Decoding (SD-SCN). The SD-SCN has been implemented using a similar FPGA used in the previous efforts and achieves two orders of magnitude higher capacity, with no error-performance penalty but with the cost of few extra clock cycles per data access.

本文言語English
ホスト出版物のタイトル2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
ページ1270-1273
ページ数4
DOI
出版ステータスPublished - 2013
外部発表はい
イベント2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
継続期間: 2013 12 32013 12 5

出版物シリーズ

名前2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Conference

Conference2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
国/地域United States
CityAustin, TX
Period13/12/313/12/5

ASJC Scopus subject areas

  • 情報システム
  • 信号処理

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