Low cost permanent fault detection using ultra-reduced instruction set co-processors
Bibliographic record
Abstract
In this paper, we propose a new, low hardware overhead solution for permanent fault detection at the microarchitecture/instruction level. The proposed technique is based on an ultra-reduced instruction set co-processor (URISC) that, in its simplest form, executes only one Turing complete instruction --- the subleq instruction. Thus, any instruction on the main core can be redundantly executed on the URISC using a sequence of subleq instructions, and the results can be compared, also on the URISC, to detect faults. A number of novel software and hardware techniques are proposed to decrease the performance overhead of online fault detection while keeping the error detection latency bounded including: (i) URISC routines and hardware support to check both control and data flow instructions; (ii) checking only a subset of instructions in the code based on a novel check window criterion; and (iii) URISC instruction set extensions. Our experimental results, based on FPGA synthesis and RTL simulations, illustrate the benefits of the proposed techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".