Split-Fabric: A Novel Wafer-Scale Hardware Obfuscation Methodology using Silicon Interconnect Fabric
Bibliographic record
Abstract
Global manufacturing of integrated circuits provides cost-effective access to high-end fabrication facilities. Offshoring intellectual property (IP) to untrusted foundries, however, makes fab-less design houses vulnerable to several issues, such as reverse engineering, overbuilding, counterfeiting, IP piracy, and insertion of malicious circuits. Several hardware security methodologies and techniques have been proposed in the past few decades to thwart hardware attacks and reduce hardware vulnerabilities.Split-Fabric, a novel wafer-scale hardware security methodology that utilizes the silicon interconnect fabric (Si-IF) technology is proposed in this paper. Split-Fabric is a secure, scalable, low-overhead, and heterogeneous hardware security methodology that supports a fully-untrusted threat model. Two benchmark circuits, a nine-stage ring-oscillator and an 8x8 SRAM array, are designed and fabricated in part using TSMC 65 nm and in part using GF 45 nm to evaluate the performance overhead of Split-Fabric at different levels of obfuscation. According to simulation results, Split-Fabric exhibits orders of magnitude lower obfuscation overhead as compared to the split manufacturing approach.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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".