Safe Overclocking of Tightly Coupled CGRAs and Processor Arrays using Razor
Bibliographic record
Abstract
Overclocking a CPU is a common practice among home-built PC enthusiasts where the CPU is operated at a higher frequency than its speed rating. This practice is unsafe because timing errors cannot be detected by modern CPUs and they can be practically undetectable by the end user. Using a timing speculation technique such as Razor, it is possible to detect timing errors in CPUs. To date, Razor has been shown to correct only unidirectional, feed-forward processor pipelines. In this paper, we safely overclock 2D arrays by extending Razor correction to cover bidirectional communication in a tightly coupled or lockstep fashion. To recover from an error, stall wavefronts are produced which propagate across the device. Multiple errors may arise in close proximity in time and space; if the corresponding stall wavefronts collide, they merge to produce a single unified wavefront, allowing recovery from multiple errors with one stall cycle. We demonstrate the correctness and viability of our approach by constructing a proof-of-concept prototype which runs on a traditional Altera FPGA. Our approach can be applied to custom computing arrays, systolic arrays, CGRAs, and also time-multiplexed FPGAs such as those produced by Tabula. As a result, these devices can be overclocked and safely tolerate dynamic, data-dependent timing errors. Alternatively, instead of overclocking, this same technique can be used to `undervolt' the power supply and save energy.
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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".