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A current-mode spiking neural classifier with lumped dendritic nonlinearity

  • Amitava Banerjee
  • , Sougata Kar
  • , Subhrajit Roy
  • , Aritra Bhaduri
  • , Arindam Basu

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

We present the current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown earlier that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with less synaptic resources than conventional algorithms. Hence, in our address event based implementation, we save 2-12X memory resources in storing connectivity information. The chip fabricated in 0.35μm CMOS has 8 dendrites per cell and uses two opposing cells per class to cancel common mode inputs. Preliminary results show the chip is functional and dissipates 30nW of static power per neuronal cell and 422pJ/spike.
Original languageEnglish
Title of host publication2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
PublisherIEEE
Pages714-717
Volume2015-July
ISBN (Print)9781479983919
DOIs
Publication statusPublished - 27 Jul 2015
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
Duration: 24 May 201527 May 2015

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2015-July
ISSN (Print)0271-4310

Conference

ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2015
PlacePortugal
CityLisbon
Period24/05/1527/05/15

Bibliographical note

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