Flexible Instrumentation for Live On-Chip Debug of Machine Learning Training on FPGAs
Bibliographic record
Abstract
FPGAs have recently shown promise for accelerating machine learning training. This has led to research into the co-design of narrow-precision accelerator architectures and the investigation of novel machine learning models. Such research can be extremely expensive, as the steep cost of training a model can increase several-fold due to the need of performing hyper-parameter tuning and adjustments to the model to ensure acceptable convergence speed and accuracy. In this scenario, monitoring key data on-chip is essential to more quickly understand and diagnose problems, significantly reducing training costs.Previous work has proposed on-chip debug instrumentation to monitor key signals for both general-purpose circuits and inference algorithms. This instrumentation either performs limited on-chip compression, or is extremely restricted in the amount of run-time customization that may occur. We argue that for training applications, the extremely long and expensive training runs warrant significantly more flexibility in the on-chip instrumentation, even at the expense of some chip area.In this paper, we propose flexible debug instrumentation that allows for the live debugging of machine learning systems during training. Different from previous debug instrumentation, our instrumentation offers firmware programmability, allowing the researcher to gather data in a large variety of ways that would likely not be anticipated at compile time.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".