Hardware architecture of the Protein Processing Associative Memory and the effects of dimensionality and quantisation on performance

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Qadir:2014:GPEM,

• author = "Omer Qadir and Alex Lenz and Gianluca Tempesti and Jon Timmis and Tony Pipe and Andy Tyrrell",
• title = "Hardware architecture of the Protein Processing Associative Memory and the effects of dimensionality and quantisation on performance",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2014",
• volume = "15",
• number = "3",
• pages = "245--275",
• month = sep,
• keywords = "genetic algorithms, genetic programming, evolvable hardware, Protein processing, PPAM, FPGA, Associative memory, BERT2, Inverse kinematics, Dimensionality, Quantisation, Non-standard computation",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-014-9217-1",
• size = "30 pages",
• 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.",

}

Genetic Programming entries for Omer Qadir Alex Lenz Gianluca Tempesti Jon Timmis Anthony Pipe Andrew M Tyrrell

Citations