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arXiv · 1402.0808

Associative Memories Based on Multiple-Valued Sparse Clustered Networks

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

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Hooman Jarollahi, Naoya Onizawa, Takahiro Hanyu, Warren J. Gross. 2014-02-03. Associative Memories Based on Multiple-Valued Sparse Clustered Networks. https://doi.org/10.1109/ismvl.2014.44

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