Case study of WebAssembly Runtimes for AI Applications on the Edge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it