Rapid, Tunable Error Detection with Execution Fingerprinting
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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 <i>fingerprinting</i> has emerged as an alternative to <i>n</i>-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 <i>fingerprint</i>.</div><div class="htmlview paragraph">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 <i>n</i>-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.</div><div class="htmlview paragraph">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.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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