Learning and real-time classification of hand-written digits with spiking neural networks

Shruti R. Kulkarni, John M. Alexiades, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing within the network, from feature extraction to classification is implemented by mimicking the basic aspects of neuronal spike initiation and propagation in the brain. The feature extraction layer of the SNN uses fixed synaptic weight maps to extract the key features of the image and the classifier layer uses the recently developed NormAD approximate gradient descent based supervised learning algorithm for spiking neural networks to adjust the synaptic weights. On the standard MNIST database images of handwritten digits, our network achieves an accuracy of 99.80% on the training set and 98.06% on the test set, with nearly 7x fewer parameters compared to the state-of-the-art spiking networks. We further use this network in a GPU based user-interface system demonstrating real-time SNN simulation to infer digits written by different users. On a test set of 500 such images, this real-time platform achieves an accuracy exceeding 97% while making a prediction within an SNN emulation time of less than 100 ms.

Original languageEnglish (US)
Title of host publicationICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-131
Number of pages4
ISBN (Electronic)9781538619117
DOIs
StatePublished - Feb 14 2018
Event24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia
Duration: Dec 5 2017Dec 8 2017

Publication series

NameICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
Volume2018-January

Other

Other24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017
Country/TerritoryGeorgia
CityBatumi
Period12/5/1712/8/17

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Keywords

  • GPU based acceleration
  • Spiking neural networks
  • classification
  • real-time processing
  • supervised learning

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