Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

Shvan Karim, Jim Harkin, Liam McDaid, Bryan Gardiner, Junxiu Liu, David Halliday, Andy Tyrrell, Jon Timmis, Alan Millard, Anju Johnson

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

2 Citations (Scopus)

Abstract

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability.
LanguageEnglish
Title of host publication 2017 IEEE Computer Society Annual Symposium on VLSI
EditorsMichael Hübner, Ricardo Reis, Mircea Stan, Nikolaos Voros
PublisherIEEE
Pages421-426
Number of pages6
ISBN (Electronic)9781509067626
ISBN (Print)9781509067633
DOIs
Publication statusPublished - 24 Jul 2017
Externally publishedYes
EventIEEE Computer Society Annual Symposium on Very-Large-Scale-Integration - Bochum, Germany
Duration: 3 Jul 20175 Jul 2017
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7985547 (Link to Conference Proceedings)

Conference

ConferenceIEEE Computer Society Annual Symposium on Very-Large-Scale-Integration
Abbreviated titleVLSI 2017
CountryGermany
CityBochum
Period3/07/175/07/17
Internet address

Fingerprint

Neurons
Field programmable gate arrays (FPGA)
Repair
Hardware
Neural networks
Astrocytes
Brain
Degradation

Cite this

Karim, S., Harkin, J., McDaid, L., Gardiner, B., Liu, J., Halliday, D., ... Johnson, A. (2017). Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. In M. Hübner, R. Reis, M. Stan, & N. Voros (Eds.), 2017 IEEE Computer Society Annual Symposium on VLSI (pp. 421-426). IEEE. https://doi.org/10.1109/ISVLSI.2017.80
Karim, Shvan ; Harkin, Jim ; McDaid, Liam ; Gardiner, Bryan ; Liu, Junxiu ; Halliday, David ; Tyrrell, Andy ; Timmis, Jon ; Millard, Alan ; Johnson, Anju. / Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. 2017 IEEE Computer Society Annual Symposium on VLSI . editor / Michael Hübner ; Ricardo Reis ; Mircea Stan ; Nikolaos Voros. IEEE, 2017. pp. 421-426
@inproceedings{7b784fdce7cd4c0682a7c80af11f778b,
title = "Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks",
abstract = "This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability.",
keywords = "self-repair, astrocytes, spiking neural networks, FPGA, bio-inspired computing",
author = "Shvan Karim and Jim Harkin and Liam McDaid and Bryan Gardiner and Junxiu Liu and David Halliday and Andy Tyrrell and Jon Timmis and Alan Millard and Anju Johnson",
year = "2017",
month = "7",
day = "24",
doi = "10.1109/ISVLSI.2017.80",
language = "English",
isbn = "9781509067633",
pages = "421--426",
editor = "Michael H{\"u}bner and Ricardo Reis and Mircea Stan and Nikolaos Voros",
booktitle = "2017 IEEE Computer Society Annual Symposium on VLSI",
publisher = "IEEE",

}

Karim, S, Harkin, J, McDaid, L, Gardiner, B, Liu, J, Halliday, D, Tyrrell, A, Timmis, J, Millard, A & Johnson, A 2017, Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. in M Hübner, R Reis, M Stan & N Voros (eds), 2017 IEEE Computer Society Annual Symposium on VLSI . IEEE, pp. 421-426, IEEE Computer Society Annual Symposium on Very-Large-Scale-Integration, Bochum, Germany, 3/07/17. https://doi.org/10.1109/ISVLSI.2017.80

Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. / Karim, Shvan; Harkin, Jim; McDaid, Liam; Gardiner, Bryan; Liu, Junxiu; Halliday, David; Tyrrell, Andy ; Timmis, Jon; Millard, Alan; Johnson, Anju.

2017 IEEE Computer Society Annual Symposium on VLSI . ed. / Michael Hübner; Ricardo Reis; Mircea Stan; Nikolaos Voros. IEEE, 2017. p. 421-426.

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

TY - GEN

T1 - Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

AU - Karim, Shvan

AU - Harkin, Jim

AU - McDaid, Liam

AU - Gardiner, Bryan

AU - Liu, Junxiu

AU - Halliday, David

AU - Tyrrell, Andy

AU - Timmis, Jon

AU - Millard, Alan

AU - Johnson, Anju

PY - 2017/7/24

Y1 - 2017/7/24

N2 - This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability.

AB - This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability.

KW - self-repair

KW - astrocytes

KW - spiking neural networks

KW - FPGA

KW - bio-inspired computing

UR - https://ieeexplore.ieee.org/document/7987556

U2 - 10.1109/ISVLSI.2017.80

DO - 10.1109/ISVLSI.2017.80

M3 - Conference contribution

SN - 9781509067633

SP - 421

EP - 426

BT - 2017 IEEE Computer Society Annual Symposium on VLSI

A2 - Hübner, Michael

A2 - Reis, Ricardo

A2 - Stan, Mircea

A2 - Voros, Nikolaos

PB - IEEE

ER -

Karim S, Harkin J, McDaid L, Gardiner B, Liu J, Halliday D et al. Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. In Hübner M, Reis R, Stan M, Voros N, editors, 2017 IEEE Computer Society Annual Symposium on VLSI . IEEE. 2017. p. 421-426 https://doi.org/10.1109/ISVLSI.2017.80