Rapid, Tunable Error Detection with Execution Fingerprinting
Bibliographic record
Abstract
Recently, the combination of semiconductor manufacturing technology scaling and pressure to reduce semiconductor system costs and power consumption has resulted in the development of computer systems responsible for executing a mix of safety-critical and non-critical tasks. However, such systems are poorly utilized if lockstep execution forces all processor cores to execute the same task even when not executing safety-critical tasks. Execution fingerprinting has emerged as an alternative to n-modular redundancy for verifying redundant execution without requiring that all cores execute the same task or even execute redundant tasks concurrently. Fingerprinting takes a bit stream characterizing the execution of a task and compresses it into a single, fixed-width word or fingerprint. Fingerprinting has several key advantages. First, it reduces redundancy-checking bandwidth by compressing changes to external state into a single, fixed-width word. Second, it reduces error detection latency by capturing and exposing intermediate operations on faulty data. Third, it naturally supports the design of mixed criticality systems by making dual-, triple-, and n-modular redundancy available without requiring significant architectural changes. Fourth, while it can't guarantee perfect error detection, error detection probabilities and latencies can be tuned to a particular application. In this paper, we describe fingerprinting in safety-critical systems and explore the various trade-offs inherent in fingerprinting subsystem design, including: (a) determining what application data to compress, as a function of error detection probability and latency, and (b) identifying a corresponding fingerprinting circuit implementation. In this context, we present several case studies demonstrating how application characteristics inform fingerprinting subsystem design.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".