Offloading Machine Learning to Programmable Data Planes: A Systematic Survey
Bibliographic record
Abstract
The demand for machine learning (ML) has increased significantly in recent decades, enabling several applications, such as speech recognition, computer vision, and recommendation engines. As applications become more sophisticated, the models trained become more complex while also increasing the amount of data used for training. Several domain-specific techniques can be helpful to scale machine learning to large amounts of data and more complex models. Among the methods employed, of particular interest is offloading machine learning functionality to the network infrastructure, which is enabled by the use of emerging programmable data plane hardware, such as SmartNICs and programmable switches. As such, offloading machine learning to programmable network hardware has attracted considerable attention from the research community in the last few years. This survey presents a study of programmable data planes applied to machine learning, also highlighting how in-network computing is helping to speed up machine learning applications. In this article, we provide various concepts and propose a taxonomy to classify existing research. Next, we systematically review the literature that offloads machine learning functionality to programmable data plane devices, classifying it based on our proposed taxonomy. Finally, we discuss open challenges in the field and suggest directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".