AODPart: Accuracy-Optimal Online Partitioning for Edge Inference with Delay Constraint
Bibliographic record
Abstract
We consider the partitioning of a deep neural network (DNN) inference job and offloading part of it from a resource-constrained device to a resource-rich server. The inference job is required to finish within a delay constraint, but it is allowed to perform early exit at some intermediate layer of the DNN, at the cost of lower accuracy. Since in practice both the processing delay and the communication delay of offloading usually are unknown ahead of time, this is naturally modelled as an online delay-constrained accuracy maximization problem. We propose Accuracy-Optimal Delay Constrained Online Partitioning (AODPart), a lightweight online algorithm that uses an adaptive thresholding strategy to solve the offloading problem. We derive the competitive ratio for AODPart and show that it is optimal in the sense that no other online algorithm can achieve a lower deterministic competitive ratio. Furthermore, we show that AODPart is robust and provides worst-case performance guarantee even with parameter estimation error. Through experimenting with common vision and language learning models, we demonstrate that AODPart substantially outperforms state-of-the-art alternatives and returns near optimal accuracy in practice.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".