Energy-Efficient Associative Memory Based on Neural Cliques

Traditional memories use an address to index the stored data. Associative memories rely on a different principle: Part of previously stored data are used to retrieve the remaining part. They are widely used, for instance, in network routers for packet forwarding. A classical way to implement such me...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2016-04, Vol.63 (4), p.376-380
Hauptverfasser: Boguslawski, Bartosz, Heitzmann, Frederic, Larras, Benoit, Seguin, Fabrice
Format: Artikel
Sprache:eng
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Zusammenfassung:Traditional memories use an address to index the stored data. Associative memories rely on a different principle: Part of previously stored data are used to retrieve the remaining part. They are widely used, for instance, in network routers for packet forwarding. A classical way to implement such memories is content-addressable memory (CAM). Since its operation is fully parallel, the response is obtained in a single clock cycle. However, this comes at the cost of energy consumption. This brief proposes to use a recent type of neural networks as a novel way to implement associative memories. Owing to an efficient retrieval algorithm guided by the information being searched, they are a good candidate for low-power associative memory. Compared to the CAM-based system, the analog implementation of 12-kb neuro-inspired memory designed for 65-nm CMOS technology offers 48% energy savings.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2015.2504946