Trueno: A Cross-Platform Machine Learning Model Serving Framework in Heterogeneous Edge Systems
Bibliographic record
Abstract
With the increasing demand of intelligent edge services(IES), diverse hardware vendors present their edge devices with vendor-specific inference frameworks, each requiring a distinct model parameter structure. Consequently, edge service developers have to deploy Artificial Intelligence (AI) models on these devices following the different frameworks, which significantly increases the learning cost and challenges the fragile development of IES. To simplify and accelerate the development of machine learning based edge services in the practical heterogeneous hardware systems, we present, Trueno1a cross-platform machine learning model serving framework. Trueno provides unified APIs and creates a less-code development environment for developers, so that models can easily adapt to different environments. Trueno has been used to support multiple real-world commercial AI edge systems, two of which will be demonstrated about its efficiency and flexibility in model deployment.1Trueno means “thunder” in Italiano; we expect this framework can help users deploy IES in their heterogeneous devices as fast as possible.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".