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• W3104049105 abstract "Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this article, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129 X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46 X reduction in latency and 77.02 X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37 X reduction in power consumption." @default.
• W3104049105 created "2020-11-23" @default.
• W3104049105 creator A5057735813 @default.
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• W3104049105 date "2020-01-01" @default.
• W3104049105 modified "2023-09-25" @default.
• W3104049105 title "Neuromorphic System for Spatial and Temporal Information Processing" @default.
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• W3104049105 doi "https://doi.org/10.1109/tc.2020.3000183" @default.
• W3104049105 hasPublicationYear "2020" @default.
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