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Hardware architecture of the protein processing associative memory and the effects of dimensionality and quantisation on performance

Qadir, Omer; Lenz, Alex; Tempesti, Gianluca; Timmis, Jon; Pipe, Tony; Tyrrell, Andy

Authors

Omer Qadir

Alex Lenz

Gianluca Tempesti

Jon Timmis

Andy Tyrrell



Abstract

The Protein Processor Associative Memory (PPAM) is a novel hardware architecture for a distributed, decentralised, robust and scalable, bidirectional, hetero-associative memory, that can adapt online to changes in the training data. The PPAM uses the location of data in memory to identify relationships and is therefore fundamentally different from traditional processing methods that tend to use arithmetic operations to perform computation. This paper presents the hardware architecture and details a sample digital logic implementation with an analysis of the implications of using existing techniques for such hardware architectures. It also presents the results of implementing the PPAM for a robotic application that involves learning the forward and inverse kinematics. The results show that, contrary to most other techniques, the PPAM benefits from higher dimensionality of data, and that quantisation intervals are crucial to the performance of the PPAM. © 2014 Springer Science+Business Media New York.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal Genetic Programming and Evolvable Machines
Print ISSN 1389-2576
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 15
Issue 3
Pages 245-274
APA6 Citation Pipe, T., Qadir, O., Lenz, A., Tempesti, G., Timmis, J., Pipe, A. G., & Tyrrell, A. (2014). Hardware architecture of the protein processing associative memory and the effects of dimensionality and quantisation on performance. Genetic Programming and Evolvable Machines, 15(3), 245-274. https://doi.org/10.1007/s10710-014-9217-1
DOI https://doi.org/10.1007/s10710-014-9217-1
Keywords protein processing, PPAM, FPGA associative memory, BERT2, inverse kinematics, dimensionality, quantisation, non-standard computation
Publisher URL http://dx.doi.org/10.1007/s10710-014-9217-1
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