NDLI: Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories

Content Provider	ACM Digital Library
Author	Coussy, Philippe Chavet, Cyrille Wouafo, Hugues Nono Conde-Canencia, Laura
Copyright Year	2015
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.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	15504832
e-ISSN	15504840
DOI	10.1145/2629510
Journal	ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number	11
Issue Number	4
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural network Associative memory Neural cliques Sparse network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture Nanoscience and Nanotechnology Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories