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, [email protected]. 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.
We replay a 60-second stream where we initially set the latency SLO to 250ms for the first half , and then increase it to 700ms for the remainder. As shown in Figure 14, under stringent latency conditions, the predictor deduces that it is impractical to schedule fine-tuning requests while adhering to the latency SLO, hence no fine-tuning requests are scheduled.
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