Patchability-Driven Design Exploration for System-on-Chip Patching Architectures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it