Online Performance Monitoring of Neuromorphic Computing Systems
Online Performance Monitoring of Neuromorphic Computing Systems
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DOI：
10.1109/ets56758.2023.10173860
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Abhishek Kumar Mishra;Anup Das;Nagarajan Kandasamy
中科院分区：
文献类型：
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作者：
Abhishek Kumar Mishra;Anup Das;Nagarajan Kandasamy
神经形态计算基于尖峰序列，其中在网络中发生的尖峰的位置和频率指导执行。本文开发了一个框架，使用基于模型的冗余来监控神经形态程序执行的正确性，其中基于软件的监视器比较映射到硬件的神经元行为与实时相应数学模型预测的神经元行为之间的差异。我们的方法减少了支持监视基础设施所需的硬件开销，并最大限度地减少了对执行应用程序的入侵。利用高保真SNN模拟器CARLSim进行的故障注入实验表明，该框架使用简洁的模型实现了高故障覆盖率，并且可以以低计算开销实时运行。
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.