Low-level trace correlation on heterogeneous 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
Tracing is a common method used to debug, analyze, and monitor various systems. Even though standard tools and tracing methodologies exist for standard and distributed environments, it is not the case for heterogeneous embedded systems. This paper proposes to fill this gap and discusses how efficient tracing can be achieved without having common system tools, such as the Linux Trace Toolkit ( LTTng ), at hand on every core. We propose a generic solution to trace embedded heterogeneous systems and overcome the challenges brought by their peculiar architectures (little available memory, bare-metal CPUs, or exotic components for instance). The solution described in this paper focuses on a generic way of correlating traces among different kinds of processors through traces synchronization , to analyze the global state of the system as a whole. The proposed solution was first tested on the Adapteva Parallella board. It was then improved and thoroughly validated on TI’s Keystone 2 System-on-Chip (SoC).
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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