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Author Coussy, Philippe ♦ Chavet, Cyrille ♦ Wouafo, Hugues Nono ♦ Conde-Canencia, Laura
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Neural network ♦ Associative memory ♦ Neural cliques ♦ Sparse network
Abstract Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational simplifications and architectural optimizations of the original GBNN. This work leads to significant complexity and area reduction without affecting neither memorizing nor retrieving performance. The obtained results open new perspectives in the design of neuromorphic hardware to support large-scale general-purpose neural algorithms.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-04-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 11
Issue Number 4
Page Count 23
Starting Page 1
Ending Page 23


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Source: ACM Digital Library