Paper Info
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.
Year
DOI
Venue
2017
10.1109/ISVLSI.2017.80
2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Keywords
Field
DocType
Self-repair,Astrocytes,Spiking neural networks,FPGA,Bio-inspired computing
MATLAB,Computer science,Bio-inspired computing,Field-programmable gate array,Novelty,Artificial neural network,Spiking neural network,Maintenance engineering,Embedded system,Self repair
Conference
ISBN
Citations 
PageRank 
978-1-5090-6763-3
0
0.34
References 
Authors
13
10
Name
Order
Citations
PageRank
Shvan Karim181.80
Jim Harkin232536.82
Liam Mcdaid327030.48
Bryan Gardiner4288.31
Junxiu Liu512523.91
David M. Halliday69515.07
Andy M. Tyrrell762973.61
Jon Timmis81237120.32
Alan G. Millard9245.94
Anju P. Johnson10395.20