A Deadlock-Free and Connectivity-Guaranteed Methodology for Achieving Fault-Tolerance in On-Chip Networks
Bibliographic record
Abstract
To improve the reliability of on-chip network based systems, we design a deadlock-free routing technique that is more resilient to component failures and guarantees a higher degree of node connectivity. The routing methodology consists of three key steps. First, we determine the maximal connected subgraph of the faulty network by checking whether the defective components happen to be the cut vertices and bridges of the network topology. A precise fault diagnosis mechanism is used to identify partial defective routers. Second, we construct an acyclic channel dependency graph that breaks all cycles and preserves connectivity of the maximal connected subgraph. This is done through the cycle-breaking and connectivity guaranteed (CBCG) algorithm. Finally, we introduce a fault-tolerant adaptive routing scheme that can be used with or without virtual channels for network congestion avoidance and high-throughput routing. The simulation results show both the effectiveness and robustness of the proposed approach. For an 8 × 8 2D-Mesh with 40 percent of link damage, full connectivity and deadlock freedom are still archived without disabling any faultless router in 98.18 percent of the simulations. In a 2D-Torus, the simulation percentage is even higher (99.93 percent). The hardware overhead for supporting the introduced features is minimal. An on-line implementation of CBCG using TSMC 65nm library has only 0.966 and 1.139 percent area overhead for the 8 × 8 and 16 × 16 2D-Meshes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".