期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 12, 页码 7126-7140出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3084250
关键词
Neuromorphics; Neurons; Fault tolerant systems; Fault tolerance; Task analysis; Context modeling; Brain modeling; Brain inspired; context-dependent learning; fault tolerant; neuromorphic computing; spiking neural network (SNN)
类别
资金
- National Natural Science Foundation of China [62071324, 62006170]
- China Postdoctoral Science Foundation [2020M680885]
This study introduces a scalable hardware framework for fault-tolerant context-dependent learning in neuromorphic computing, demonstrating an improvement in network throughput. The proposed system can be utilized for real-time decision-making, brain-machine integration, and research on brain cognition during learning.
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.
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