Compressing Data and Deep Learning Models for Green Edge Computing
Bibliographic record
Abstract
The rapid expansion of Artificial Intelligence (AI) applications at the Edge has created an increasing demand for energy-efficient deployment. Furthermore, Edge devices are inherently constrained in computation, bandwidth, and storage, making the exploration of compressed AI models and data a worthwhile approach to enhancing efficiency. While compression improves resource and energy efficiency, it often leads to significant performance degradation, especially with complex data. To address this challenge, we propose leveraging transformations on compressed data to enhance the effectiveness of compressed AI models. We evaluate 13 different transformations across three benchmark datasets (MNIST, FashionMNIST, CIFAR-10) and find that applying shadow transformations to lossy compressed images significantly mitigates performance loss. Based on experiments in a real edge device our approach achieves a 10.7% reduction in energy consumption, 99.48% compression in AI model architecture, and up to 72% data size reduction, with performance degradation ranging only from 0.14% to 0.89%. These results show that data transformations can enable efficient Edge AI inference with minimal performance loss, reducing energy, bandwidth, and computation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".