Spiking neural network based ASIC for character recognition

Shruti R. Kulkarni, Maryam Shojaei Baghini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Spiking neural networks are the recent models of artificial neural networks. These networks use biologically similar neuron models as their basic computation units. This paper presents and compares a custom spiking neural network (SNN) with a conventional nearest neighbour classifier for hand written character recognition. The classifiers are designed and simulated in 90nm CMOS technology. The two algorithms are compared in terms of their success rates and their hardware requirements (based on the area and power estimates). The classification performance of the SNN is also compared with that of second generation feedforward neural network, with the same set of images. The robustness of SNN is demonstrated in this work by its ability to classify the 30 out of 32 noisy characters images presented as compared to the nearest neighbour algorithm, which correctly classified only 20 of them. The feedforward neural network using backpropagation algorithm was able to correctly identify 29 out of 32 noisy images in MATLAB. In terms of hardware, the ASIC realizing the nearest neighbour classifier dissipates power of 1.2mW and an area of 380μm × 380μm, while the SNN dissipates 16.7mW power and an area of 1mm × 1mm. The higher area and power requirements for the SNN stem from its inherent parallel architecture. Earlier works have focused on realization of a single spiking neuron and its variants while this work brings about the application using networks of these neurons and their suitability for custom realization.

Original languageEnglish
Title of host publicationProceedings - 2013 9th International Conference on Natural Computation, ICNC 2013
PublisherIEEE Computer Society
Pages194-199
Number of pages6
ISBN (Print)9781467347143
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 9th International Conference on Natural Computation, ICNC 2013 - Shenyang, China
Duration: Jul 23 2013Jul 25 2013

Publication series

NameProceedings - International Conference on Natural Computation
ISSN (Print)2157-9555

Conference

Conference2013 9th International Conference on Natural Computation, ICNC 2013
Country/TerritoryChina
CityShenyang
Period07/23/1307/25/13

Keywords

  • ASIC design
  • Spiking Neural networks
  • character recognition

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