Distributed Inference with Deep Learning Models across Heterogeneous Edge Devices
Why this work is in the frame
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Bibliographic record
Abstract
Recent years witnessed an increasing research attention in deploying deep learning models on edge devices for inference. Due to limited capabilities and power constraints, it may be necessary to distribute the inference workload across multiple devices. Existing mechanisms divided the model across edge devices with the assumption that deep learning models are constructed with a chain of layers. In reality, however, modern deep learning models are more complex, involving a directed acyclic graph (DAG) rather than a chain of layers.In this paper, we present EdgeFlow, a new distributed inference mechanism designed for general DAG structured deep learning models. Specifically, EdgeFlow partitions model layers into independent execution units with a new progressive model partitioning algorithm. By producing near-optimal model partitions, our new algorithm seeks to improve the run-time performance of distributed inference as these partitions are distributed across the edge devices. During inference, EdgeFlow orchestrates the intermediate results flowing through these units to fulfill the complicated layer dependencies. We have implemented Edge-Flow based on PyTorch, and evaluated it with state-of-the-art deep learning models in different structures. The results show that EdgeFlow reducing the inference latency by up to 40.2% compared with other approaches, which demonstrates the effectiveness of our design.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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 it