Conference proceeding
Online Performance Monitoring of Neuromorphic Computing Systems
Proceedings - European Test Workshop, v 2023-, pp 1-4
22 May 2023
Abstract
Neuromorphic computation is based on spike trains in which the location and frequency of spikes occurring within the network guide the execution. This paper develops a frame-work to monitor the correctness of a neuromorphic program's execution using model-based redundancy in which a software-based monitor compares discrepancies between the behavior of neurons mapped to hardware and that predicted by a corresponding mathematical model in real time. Our approach reduces the hardware overhead needed to support the monitoring infrastructure and minimizes intrusion on the executing application. Fault-injection experiments utilizing CARLSim, a high-fidelity SNN simulator, show that the framework achieves high fault coverage using parsimonious models which can operate with low computational overhead in real time.
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Details
- Title
- Online Performance Monitoring of Neuromorphic Computing Systems
- Creators
- Abhishek Kumar Mishra - Drexel UniversityAnup Das - Drexel UniversityNagarajan Kandasamy - Drexel University,ECE Department,Philadelphia,USA
- Publication Details
- Proceedings - European Test Workshop, v 2023-, pp 1-4
- Publisher
- IEEE
- Number of pages
- 4
- Grant note
- National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001032757100001
- Scopus ID
- 2-s2.0-85166260113
- Other Identifier
- 991022207159904721