Literature DB >> 31865886

A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing.

A Serb1, I Kobyzev2, J Wang1, T Prodromakis1.   

Abstract

One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathematical machinery underlying cognition in a way that is very efficiently implementable in hardware. In this work, we propose a 'semi-holographic' representation system that can be implemented in hardware using only multiplexing and addition operations, thus avoiding the need for expensive multiplication. The resulting architecture can be readily constructed by recycling standard microprocessor elements and is capable of performing two key mathematical operations frequently used in cognition, superposition and binding, within a budget of below 6 pJ for 64-bit operands. Our proposed 'cognitive processing unit' is intended as just one (albeit crucial) part of much larger cognitive systems where artificial neural networks of all kinds and associative memories work in concord to give rise to intelligence. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Keywords:  hardware; hyperdimensional; processor

Year:  2019        PMID: 31865886      PMCID: PMC6939245          DOI: 10.1098/rsta.2019.0162

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  10 in total

1.  Key and recollection vector effects on heteroassociative memory performance.

Authors:  D Casasent; B Telfer
Journal:  Appl Opt       Date:  1989-01-15       Impact factor: 1.980

2.  Optical implementations of associative networks with versatile adaptive learning capabilities.

Authors:  A D Fisher; W L Lippincott; J N Lee
Journal:  Appl Opt       Date:  1987-12-01       Impact factor: 1.980

3.  Holographic reduced representations.

Authors:  T A Plate
Journal:  IEEE Trans Neural Netw       Date:  1995

Review 4.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

5.  Universal Approximation Using Radial-Basis-Function Networks.

Authors:  J Park; I W Sandberg
Journal:  Neural Comput       Date:  1991       Impact factor: 2.026

6.  LSTM: A Search Space Odyssey.

Authors:  Klaus Greff; Rupesh K Srivastava; Jan Koutnik; Bas R Steunebrink; Jurgen Schmidhuber
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2016-07-08       Impact factor: 10.451

7.  Some algebraic relations between involutions, convolutions, and correlations, with applications to holographic memories.

Authors:  P H Schönemann
Journal:  Biol Cybern       Date:  1987       Impact factor: 2.086

8.  Non-holographic associative memory.

Authors:  D J Willshaw; O P Buneman; H C Longuet-Higgins
Journal:  Nature       Date:  1969-06-07       Impact factor: 49.962

9.  A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

Authors:  Ning Qiao; Hesham Mostafa; Federico Corradi; Marc Osswald; Fabio Stefanini; Dora Sumislawska; Giacomo Indiveri
Journal:  Front Neurosci       Date:  2015-04-29       Impact factor: 4.677

10.  Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Authors:  Courtney J Spoerer; Patrick McClure; Nikolaus Kriegeskorte
Journal:  Front Psychol       Date:  2017-09-12
  10 in total

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