Pitstop: Enabling a Virtual Network Free Network-on-Chip
Bibliographic record
Abstract
Maintaining correctness is of paramount importance in the design of a computer system. Within a multiprocessor interconnection network, correctness is guaranteed by having deadlock-free communication at both the protocol and network levels. Modern network-on-chip (NoC) designs use multiple virtual networks to maintain protocol-level deadlock freedom, at the expense of high power and area overheads. Other techniques involve complex detection and recovery mechanisms, or use misrouting which incurs additional packet latency. Considering that the probability of deadlocks occurring is low, the additional resources needed to avoid/resolve deadlocks should also be low. To this end, we propose Pitstop, a low-cost technique that guarantees correctness by resolving both protocol and network-level deadlocks without the use of virtual networks, complex hardware, or misrouting. Pitstop transfers blocked packets to the network interface (NI) creating a bubble (empty buffer slot) which breaks deadlock. The blocked packet can make forward progress through NI to NI traversals using low complexity bypassing mechanisms. This scheme performs better due to higher utilization of virtual channels and works on arbitrary irregular topologies without any virtual networks. Compared to state-of-the-art solutions, Pitstop can improve performance up to 11% and reduce power and area up to 41% and 40%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".