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• W2897961390 abstract "Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timingdependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and communicate through the NoC. This enables chip-level interconnection, scalability and reconfigurability necessary for deploying different applications. The hardware is applied to learn MNIST digits as an evaluation of its learning capability. We explore the design space to study the trade-offs between speed, area and energy. How to use this procedure to find optimal architecture configuration is also discussed." @default.
• W2897961390 created "2018-10-26" @default.
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• W2897961390 date "2018-07-01" @default.
• W2897961390 modified "2023-09-24" @default.
• W2897961390 title "Scalable NoC-based Neuromorphic Hardware Learning and Inference" @default.
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• W2897961390 doi "https://doi.org/10.1109/ijcnn.2018.8489619" @default.
• W2897961390 hasPublicationYear "2018" @default.
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