Patchability-Driven Design Exploration for System-on-Chip Patching Architectures
Bibliographic record
Abstract
As System-on-Chip (SoC) designs become increasingly complex, ensuring comprehensive verification has become more challenging, leading to overlooked hardware bugs that can be found in the field. Addressing hardware bugs post-deployment is difficult, as they typically cannot be easily fixed like software bugs. To tackle this issue, hardware-based patching mechanisms have emerged as a potential solution for providing in-field fixes. However, the lack of a standardized method to evaluate the ”patchability” of different designs complicates the integration of patching infrastructure into SoCs. In this article, we propose a fully parameterized Patch Support Block (PSB) architecture that can be tailored for various hardware designs, enabling post-deployment patching. We introduce a novel patchability score formulation that provides a quantifiable metric for evaluating the effectiveness of patching designs. Our approach considers both the observability and controllability of the patching hardware and provides a framework for system integrators to maximize patchability while managing resource constraints. Through experimentation with multiple design configurations, we demonstrate how our methodology can enhance patchability in hardware systems and provide security-related fixes for SoCs in real-world scenarios.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".