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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Minghao Yan, University of Wisconsin-Madison;
(2) Hongyi Wang, Carnegie Mellon University;
(3) Shivaram Venkataraman, myan@cs.wisc.edu.
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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. Predictor Analysis
8 CONCLUSION
In this work, we examine the unique characteristics of energy consumption in neural network inference, especially for edge devices. We identified unique tradeoffs and dimensions between energy consumption and inference latency SLOs and empirically demonstrated hidden components in optimizing energy consumption. We then propose an optimization framework that automatically and holistically tunes various hardware components to find a configuration aligned with the Pareto Frontier. We empirically verify the effectiveness and efficiency of PolyThrottle. PolyThrottle also adapts to the need for fine-tuning and proposes a simple performance prediction model to adaptively schedule finetuning requests while keeping the online inference workload under the inference latency SLO whenever possible. We hope our study sheds more light on the hidden dimension of NN energy optimization.
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