# Carbon Intensity-Aware Adaptive Inference of DNNs

arXiv: https://arxiv.org/abs/2403.15824 · HTML: https://mlsanity.com/paper/2403.15824/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Analysing Environmental Efficiency in AI for X-Ray Diagnosis](https://mlsanity.com/paper/2511.07436/) (2025)

## Abstract (arXiv)

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%.