Spike Counts Based Low Complexity Learning with Binary Synapse

Hoyoung Tang, Heetak Kim, Donghyeon Cho, Jongsun Park

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

Abstract

Two main difficulties encountered when implementing the neuromorphic system that supports pre- and post-synaptic textbf spike timing based real-time learning, are 1) huge memory size to store synaptic weights and 2) large amount of computations to update the synaptic weights. In this paper, we present a textbf spike counts based learning method that can significantly relieve the hardware burden of the real time unsupervised learning process. The novel learning approach uses both pre- and post-synaptic textbf spike counts as decision metrics in the following two ways: First, the synaptic weights are updated following the proposed simplified mean-based weight update rules, where binary feature images are produced based on pre-synaptic spike counts. Using the feature image, 1 bit synaptic weights are updated only once for each input image. When updating the weights, the post-synaptic spiking counts are also used to select the most active excitatory neuron. The actual weight updates are performed only for the weights connected to the dominant excitatory neuron, leading to further reduction of the weight updates without sacrificing accuracy. The simulation results show that using only 1 bit synaptic weights with 400 output neurons, the proposed learning approach achieves 82 % recognition accuracy with MNIST test set. In addition, the number of weight updates is reduced by 15.3 times compared to the state-of-the-art learning method.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-July
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 2018 Oct 10
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 2018 Jul 82018 Jul 13

Other

Other2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period18/7/818/7/13

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Keywords

  • 1 bit synaptic weight
  • Mean-based learning
  • On-chip Learning
  • Spiking Neural Network
  • Unsupervised Learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Tang, H., Kim, H., Cho, D., & Park, J. (2018). Spike Counts Based Low Complexity Learning with Binary Synapse. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings (Vol. 2018-July). [8489486] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489486