Carbon Intensity-Aware Adaptive Inference of DNNs

arXiv:2403.15824v1 Announce Type: new
Abstract: 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 …

abstract accuracy algorithm arxiv carbon carbon footprint consumption cs.ai cs.lg dnn energy inference intensity low sustainable type