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

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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.

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Notes

  1. http://www.eEvolved.info/blog.html#PPAMverilogHDL.

  2. http://www.xilinx.com/univ/xupv5-lx110t.htm.

  3. Self-healing cellular Architectures for Biologically-inspired highly Reliable Electronic systems.

  4. www.brl.ac.uk.

  5. Calculate the position (and orientation) of a robotic arm from its joint angles.

  6. Calculate the joint angles from the position (and orientation) of a robotic arm.

  7. www.vicon.com.

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Acknowledgments

The research was funded by the EPSRC funded SABRE (Self-healing cellular Architectures for Biologically-inspired highly Reliable Electronic systems) project under Grant No. FP/F06219211.

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Authors and Affiliations

  1. Nordic Semiconductor, Otto Neilsens v12, Trondheim, Norway

    Omer Qadir

  2. University of York, York, UK

    Gianluca Tempesti, Jon Timmis & Andy Tyrrell

  3. University of West of England, Bristol, UK

    Alex Lenz & Tony Pipe

Authors
  1. Omer Qadir
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  2. Alex Lenz
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  3. Gianluca Tempesti
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  4. Jon Timmis
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  6. Andy Tyrrell
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Correspondence to Omer Qadir.

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Qadir, O., Lenz, A., Tempesti, G. et al. Hardware architecture of the Protein Processing Associative Memory and the effects of dimensionality and quantisation on performance. Genet Program Evolvable Mach 15, 245–274 (2014). https://doi.org/10.1007/s10710-014-9217-1

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