Prototyping a Biologically Plausible Neuron Model on a Heterogeneous CPU-FPGA Board

Kaleb Alfaro-Badilla, Alfonso Chacon-Rodriguez, Georgios Smaragdos, Christos Strydis, Andres Arroyo-Romero, Javier Espinoza-Gonzalez, Carlos Salazar-Garcia

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

5 Scopus citations

Abstract

A heterogeneous hardware-software system implemented on an Avnet ZedBoard Zynq SoC platform, is proposed for the computation of an extended Hodgkin Huxley (eHH), biologically plausible neural model. SoC's ARM A9 is in charge of handling execution of a single neuron as defined in the eHH model, each with a O(N) computational complexity, while the computation of the gap-junctions interactions for each cell is offloaded on the SoC's FPGA, cutting its O(N2) complexity by exploiting parallel-computing hardware techniques. The proposed hw-sw solution allows for speed-ups of about 18 times visa-vis à vectorized software implementation on the SoC's cores, and is comparable to the speed of the same model optimized for a 64-bit Intel Quad Core i7, at 3.9GHz.

Original languageEnglish
Title of host publication2019 IEEE 10th Latin American Symposium on Circuits and Systems, LASCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-8
Number of pages4
ISBN (Electronic)9781728104522
DOIs
StatePublished - 14 Mar 2019
Event10th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2019 - Armenia, Colombia
Duration: 24 Feb 201927 Feb 2019

Publication series

Name2019 IEEE 10th Latin American Symposium on Circuits and Systems, LASCAS 2019 - Proceedings

Conference

Conference10th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2019
Country/TerritoryColombia
CityArmenia
Period24/02/1927/02/19

Keywords

  • Biologically accurate neural networks models
  • hardware-software co-design
  • heterogeneous systems
  • high level synthesis
  • spiking neural networks
  • systems on chip

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