Carbon Intensity-Aware Adaptive Inference of DNNs
Jiwan Jung
DNN inference, known for its significant energy consumption and the resulting
high carbon footprint, can be made more sustainable by adapting model size and
accuracy to the varying carbon intensity throughout the day. Our heuristic
algorithm uses larger, high-accuracy models during low-intensity periods and
smaller, lower-accuracy ones during high-intensity periods. We also introduce a
metric, carbon-emission efficiency, which quantitatively measures the efficacy
of adaptive model selection in terms of carbon footprint. The evaluation showed
that the proposed approach could improve the carbon emission efficiency in
improving the accuracy of vision recognition services by up to 80%.