A Predictable Execution Model for COTS-Based Embedded Systems
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
Building safety-critical real-time systems out of inexpensive, non-real-time, COTS components is challenging. Although COTS components generally offer high performance, they can occasionally incur significant timing delays. To prevent this, we propose controlling the operating point of each shared resource (like the cache, memory, and interconnection buses) to maintain it below its saturation limit. This is necessary because the low-level arbiters of these shared resources are not typically designed to provide real-time guarantees. In this work, we introduce a novel system execution model, the Predictable Execution Model (PREM), which, in contrast to the standard COTS execution model, coschedules at a high level all active components in the system, such as CPU cores and I/O peripherals. In order to permit predictable, system-wide execution, we argue that real-time embedded applications should be compiled according to a new set of rules dictated by PREM. To experimentally validate our theory, we developed a COTS-based PREM testbed and modified the LLVM Compiler Infrastructure to produce PREM-compatible executables.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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