OperaV1, Main, Exploration, bibRecord, 000107

Trainable hardware for dynamical computing using error backpropagation through physical media.

Identifieur interne : 000107 ( Main/Exploration ); précédent : 000106; suivant : 000108

Trainable hardware for dynamical computing using error backpropagation through physical media.

Auteurs : Michiel Hermans [Belgique] ; Michaël Burm [Belgique] ; Thomas Van Vaerenbergh [Belgique] ; Joni Dambre [Belgique] ; Peter Bienstman [Belgique]

Source :

Nature communications ; 2015.

RBID : pubmed:25801303

Abstract

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

DOI: 10.1038/ncomms7729
PubMed: 25801303

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.</div>
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Trainable hardware for dynamical computing using error backpropagation through physical media.

Trainable hardware for dynamical computing using error backpropagation through physical media.

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Abstract

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