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

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

doi:10.1109/ISVLSI.2017.80

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

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

8 Citations (SciVal)

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.

Original language	English
Title of host publication	Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
Editors	Ricardo Reis, Mircea Stan, Michael Huebner, Nikolaos Voros
Publisher	IEEE Press
Pages	421-426
Number of pages	6
ISBN (Electronic)	9781509067626
DOIs	https://doi.org/10.1109/ISVLSI.2017.80
Publication status	Published - 20 Jul 2017
Event	2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 - Bochum, North Rhine-Westfalia, Germany Duration: 03 Jul 2017 → 05 Jul 2017

Publication series

Name	Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume	2017-July
ISSN (Print)	2159-3469
ISSN (Electronic)	2159-3477

Conference

Conference	2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
Country/Territory	Germany
City	Bochum, North Rhine-Westfalia
Period	03 Jul 2017 → 05 Jul 2017

Keywords

Astrocytes
Bio-inspired computing
FPGA
Self-repair
Spiking neural networks

Access to Document

10.1109/ISVLSI.2017.80

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Karim, S., Harkin, J., McDaid, L., Gardiner, B., Liu, J., Halliday, D. M., Tyrrell, A. M., Timmis, J., Millard, A., & Johnson, A. (2017). Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. In R. Reis, M. Stan, M. Huebner, & N. Voros (Eds.), Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 (pp. 421-426). [7987556] (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI; Vol. 2017-July). IEEE Press. https://doi.org/10.1109/ISVLSI.2017.80

Karim, Shvan ; Harkin, Jim ; McDaid, Liam et al. / Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017. editor / Ricardo Reis ; Mircea Stan ; Michael Huebner ; Nikolaos Voros. IEEE Press, 2017. pp. 421-426 (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI).

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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.",

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note = "Funding Information: The authors would like to acknowledge the EPSRC funding council grant (EP/N00714X/1 & EP/N007050/1) and Ulster University for supporting this research. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 ; Conference date: 03-07-2017 Through 05-07-2017",

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Karim, S, Harkin, J, McDaid, L, Gardiner, B, Liu, J, Halliday, DM, Tyrrell, AM, Timmis, J, Millard, A & Johnson, A 2017, Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. in R Reis, M Stan, M Huebner & N Voros (eds), Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017., 7987556, Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI, vol. 2017-July, IEEE Press, pp. 421-426, 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017, Bochum, North Rhine-Westfalia, Germany, 03 Jul 2017. 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 et al.
Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017. ed. / Ricardo Reis; Mircea Stan; Michael Huebner; Nikolaos Voros. IEEE Press, 2017. p. 421-426 7987556 (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI; Vol. 2017-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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AU - Gardiner, Bryan

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AU - Halliday, David M.

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AU - Timmis, Jon

AU - Millard, Alan

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N1 - Funding Information: The authors would like to acknowledge the EPSRC funding council grant (EP/N00714X/1 & EP/N007050/1) and Ulster University for supporting this research. Publisher Copyright: © 2017 IEEE.

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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.

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Karim S, Harkin J, McDaid L, Gardiner B, Liu J, Halliday DM et al. Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. In Reis R, Stan M, Huebner M, Voros N, editors, Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017. IEEE Press. 2017. p. 421-426. 7987556. (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI). doi: 10.1109/ISVLSI.2017.80

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

Abstract

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Keywords

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