Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing
Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent l...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2022-12, Vol.33 (12), p.7126-7140 |
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| creator | Yang, Shuangming Wang, Jiang Deng, Bin Azghadi, Mostafa Rahimi Linares-Barranco, Bernabe |
| description | Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning. |
| doi_str_mv | 10.1109/TNNLS.2021.3084250 |
| format | Article |
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However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. 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| ispartof | IEEE transaction on neural networks and learning systems, 2022-12, Vol.33 (12), p.7126-7140 |
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| subjects | Algorithms Brain Brain - physiology Brain inspired Brain modeling Cognition Cognitive tasks Computational neuroscience Computers Context Context modeling context-dependent learning Decision making Fault tolerance fault tolerant Fault tolerant systems Firing pattern Hardware Learning Nervous system Neural networks Neural Networks, Computer Neuromorphic computing Neuromorphics Neurons Neurons - physiology Nodes Real time Spiking spiking neural network (SNN) Task analysis |
| title | Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing |
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