Case study of WebAssembly Runtimes for AI Applications on the Edge
Bibliographic record
Abstract
In the realm of Artificial Intelligence (AI), the need for immediate response times has given rise to the Cloud Edge Computing Continuum (CECC). This new paradigm, aided by emerging technologies, addresses latency and network delays while promoting portability, security, and efficiency, thereby enhancing Quality of Service (QoS). A noteworthy technology in this context is WebAssembly (Wasm), originally conceived to amplify web performance. It has transitioned to the CECC, primarily due to key enablers like the WebAssembly System Interface (Wasi) and the Wasm runtime. Besides offering heightened security through its sandboxing mechanism, WebAssembly's compact code paves the way for rapid cold start times and seamless migration in AI applications. However, with WebAssembly's nascent integration into the CECC, several questions arise. Prominent among them is the efficiency of deploying AI tasks in Wasm binary format, particularly the performance of Wasm runtimes in AI-centric tasks and potential factors affecting such executions. Addressing these queries, our study examines various deep-learning models on standalone WebAssembly runtimes. Our findings indicate that, for smaller networks with optimized parameters, standalone runtimes approach native performance, presenting just a 1.1x overhead on average. Contrarily, networks with an extensive parameter set exhibited pronounced overheads. We also identified multiple factors, associated both with run-times and neural networks, offering insights for future research endeavors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".