Boosting Domain-Specific Debug Through Inter-frame Compression
Bibliographic record
Abstract
Acceleration of machine learning models is proving to be an important application for FPGAs. Unfortunately, debugging such models during training or inference is difficult. Software simulations of a machine learning system may be of insufficient detail to provide meaningful debug insight, or may require infeasibly long run-times. Thus, it is often desirable to debug the accelerated model while it is running on real hardware. Effective on-chip debug often requires instrumenting a design with additional circuitry to store run-time data, consuming valuable chip resources. Previous work has developed methods to perform lossy compression of signals by exploiting machine learning specific knowledge, thereby increasing the amount of debug context that can be stored in an on-chip trace buffer. However, all prior work compresses each successive element in a signal of interest independently. Since debug signals may have temporal similarity in many machine learning applications there is an opportunity to further increase trace buffer utilization. In this paper, we present an architecture to perform lossless temporal compression in addition to the existing lossy element-wise compression. We show that, when applied to a typical machine learning algorithm in realistic debug scenarios, we are able to store twice as much information in an on-chip buffer while increasing the total area of the debug instrument by approximately 25%. The impact is that, for a given instrumentation budget, a significantly larger trace window is available during debug, possibly allowing a designer to narrow down the root cause of a bug faster.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".