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- W4316252352 abstract "Nowadays, edge computing is widely adopted to resolve the emerging deep neural networks (DNNs)-driven intelligence scenarios with the requirement of low-latency and high-accuracy, which includes heterogeneous end devices and DNNs. In such scenarios, the influx of data and computation into a shared edge server incurs prohibitive latency. Thus, we exploit the advantage of Multi-exit DNNs (ME-DNNs) that tasks can exit early at appropriate depths to save inference time. However, naively using ME-DNNs in the heterogeneous edge still fails to deliver fast inference due to improper model surgery and resource allocation." @default.
- W4316252352 created "2023-01-15" @default.
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- W4316252352 date "2022-08-29" @default.
- W4316252352 modified "2023-09-26" @default.
- W4316252352 title "Enabling Latency-Sensitive DNN Inference via Joint Optimization of Model Surgery and Resource Allocation in Heterogeneous Edge" @default.
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- W4316252352 doi "https://doi.org/10.1145/3545008.3545071" @default.
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