Investigating the Feasibility of eFPGA-based Hardware Patching
Bibliographic record
Abstract
System-on-Chip (SoC) designs are becoming increasingly complex, with the ability to detect and address all possible bugs at design time is highly challenging. Thus, to improve the survivability of SoC designs, it is desirable to be able to patch newly discovered design bugs or potential vulnerabilities in the field. Recently, the idea of hardware based patching, especially of hardware bugs, has emerged as a complementary approach to software/firmware-based post deployment updates. In anticipating potential problems, designers must invest an upfront cost to implement hardware-based patching infrastructures. In this paper, We investigate the feasibility of incorporating an embedded field-programmable gate array (eFPGA) fabric as an approach to enable hardware-based patching, i.e., reprogrammable hardware to patch hardware bugs. We propose, discuss, and evaluate three integration design architectures, characterizing the potential area and performance costs for each patching architecture and providing insights into how such architectures might be used to patch hardware bugs. Through a case study on an OpenPiton-based SoC, our results show the architectures’ area overhead costs ranging from $4.78 \%$ to $56.17 \%$, with latency arising from our example patches ranging from 1 to 2 cycles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".