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• W4313484726 abstract "Neuromorphic architectures implement low-power machine learning applications using spike-based biological neuron models trained with bio-inspired or machine learning algorithms. Prior work on simulating Spiking Neural Networks (SNNs) focused on simulating emerging compute in-memory (CIM) architectures, while prior work on mapping SNNs focused mainly on minimizing inter-core communication or resource utilization and targeted either emerging CIM architectures or specific target platforms. SNN mapping choices on a neuromoprhic multi-processor platform can impact performance and energy consumption. In this paper, we introduce a simulation framework that evaluates application mapping on a user-defined NoC-based multi-core digital neuromorphic architecture. Our simulator evaluates latency and energy based on mapping and abstract spike activity traces which indicate the firing of neurons at specific discrete timesteps defined by the application. We create two hardware models based on reported work in literature and show the evaluation of different mapping scenarios for a state-of-the-art SNN benchmark." @default.
• W4313484726 created "2023-01-06" @default.
• W4313484726 creator A5032436846 @default.
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• W4313484726 date "2022-08-01" @default.
• W4313484726 modified "2023-09-26" @default.
• W4313484726 title "DNAsim: Evaluation Framework for Digital Neuromorphic Architectures" @default.
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• W4313484726 doi "https://doi.org/10.1109/dsd57027.2022.00065" @default.
• W4313484726 hasPublicationYear "2022" @default.
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