A Fast Edge-Based Synchronizer for Tasks in Real-Time Artificial Intelligence Applications
Bibliographic record
Abstract
Real-time artificial intelligence (AI) applications mapped on edge computing need to perform data capture, data processing/intelligence extraction, and device actuation within some given time bounds. Synchronization across devices is an important problem that needs to be solved at different stages of an AI application. Synchronized data capture reduces the amount of time required in preprocessing data (data aggregation, data cleaning, missing data handling, etc). In the data processing phase, synchronization is key in ensuring convergence, accuracy, and speed of the distributed training process across multiple edge devices. The actuation phase in some cases requires certain actions to be performed at the same time on different devices. In this article, we develop a fast edge-based synchronization scheme that can time-align the execution of input-output tasks as well compute tasks. The primary idea of the fast synchronizer is to cluster the devices into groups that remain closely synchronized in their task executions and statically determine synchronization points using a game-theoretic solver. The cluster of devices uses a late notification protocol to select the best point among the precomputed synchronization points to reach a time-aligned task execution as quickly as possible. We evaluate the performance of our synchronization scheme using trace-driven simulations, and we compare the performance with existing distributed synchronization schemes for real-time AI application tasks. We implement our synchronization scheme and compare its testing accuracy and training time with other parameter server synchronization frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".