Pointer Based Routing Scheme for On-chip Learning in Neuromorphic Systems

Vladimir Kornijcuk, Doo Seok Jeong

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

Abstract

A look-up table (LUT)-based spike-routing approach is often used in inference-only neuromorphic systems due to its excellent reconfigurability. The challenge is to apply this approach also to on-chip learning that requires a search of a lengthy LUT for all relevant synapses to a firing neuron. To solve this issue, we propose a pointer-based routing scheme that remarkably accelerates spike-routing at the cost of an additional LUT (pointer LUT). Our theoretical estimations suggest that the proposed routing scheme at 1 GHz clock speed supports a spiking neural network of up to 107 synapses and more than 105 neurons firing at 50 Hz without spike traffic congestion. The scheme needs approximately 32 MB memory. To verify experimentally, the proposed routing scheme was implemented on a Xilinx Virtex 7 FPGA board deploying an array of leaky integrate-and-fire neurons.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
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

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Other

Other2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period18/07/818/07/13

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Keywords

  • LUT-based spike-routing
  • neuromorphic system
  • on-chip learning
  • spiking neural network

Cite this

Kornijcuk, V., & Jeong, D. S. (2018). Pointer Based Routing Scheme for On-chip Learning in Neuromorphic Systems. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings [8489501] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489501