Summary of Adaptive Resolution Inference (ari): Energy-efficient Machine Learning For Internet Of Things, by Ziheng Wang et al.
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
by Ziheng Wang, Pedro Reviriego, Farzad Niknia, Javier Conde, Shanshan Liu, Fabrizio Lombardi
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach called adaptive resolution inference (ARI) to optimize machine learning model performance on Internet of Things devices with limited energy and computation resources. ARI enables evaluating new tradeoffs between energy dissipation and model performance by running inferences with reduced precision and using the margin over the decision threshold to determine when to run the full model. This approach can significantly reduce computation and energy while maintaining model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ARI approach is simple: it runs inferences with reduced precision (quantization) and uses the margin over the decision threshold to decide if the result is reliable or if the full model must be used. Since quantization only introduces small deviations in inference scores, if the scores have a sufficient margin over the decision threshold, it’s unlikely that the full model would produce a different result. This means most inferences can run with the reduced precision model and only a small fraction requires the full model. |
Keywords
» Artificial intelligence » Inference » Machine learning » Precision » Quantization
