Hardware Partitioning for Vision Analytics-based Systems: Performance and Trade-offs
Bibliographic record
Abstract
Mobile Vision Analytics (MVA) is essential for advancing applications, which analyze visual data using deep neural network (DNN) models to extract contextual insights. While cloud based approach is common, the high latency incurred in transmission leading to a shift toward edge computing. However, the limited resource on edge leads to the challenge of concurrent DNN execution on shared GPU. This paper proposes a novel mathematical framework that models MVA workflows over an edge infrastructure, including the partitioning of GPU resources for concurrent DNNs. Our model integrates local and remote processing steps, accounting for wireless transmission and GPU sharing, and provides a structured approach to estimating per-step time consumption. A detailed numerical evaluation demonstrates the latency and resource utilization of each part of the system under varied scenarios, offering insights for the design of latency-sensitive MVA solutions with concurrent models on shared edge GPU infrastructure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".