This paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Minghao Yan, University of Wisconsin-Madison; Hongyi Wang, Carnegie Mellon University; Shivaram Venkataraman, myan@cs.wisc.edu. Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details B. Experimental Results C. Arithmetic Intensity D.
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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Experimental ResultsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: OpportunitiesThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: MotivationThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Arithmetic IntensityThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: ExperimentsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Hardware DetailsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »