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Neural Comput. 2016 Oct;28(10):2011-44. doi: 10.1162/NECO_a_00882. Epub 2016 Aug 24.

Energy-Efficient Neuromorphic Classifiers.

Author information

1
Département d'Études Cognitives, École Normale Supérieure-PSL Research University, 75005 Paris, France; Institut Nationale de la Santé et de la Recherche Médicale, 75005 Paris, France; and Center for Theoretical Neuroscience, Columbia University, College of Physicians and Surgeons, New York, NY 10032, U.S.A. daniel.marti@ens.fr.
2
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A., and Center for Theoretical Neuroscience, Columbia University, College of Physicians and Surgeons, New York, NY 10032, U.S.A. mr2666@columbia.edu.
3
Department of Electrical Engineering, Columbia University, New York, NY 10027, U.S.A. ms4415columbia.edu.
4
Center for Theoretical Neuroscience and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, College of Physicians and Surgeons, New York, NY 10032, U.S.A. sf2237@columbia.edu.

Abstract

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

PMID:
27557100
DOI:
10.1162/NECO_a_00882
[Indexed for MEDLINE]

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