Dodec: Random-Link, Low-Radix On-Chip Networks
Bibliographic record
Abstract
Network topology plays a vital role in chip design, it largely determines network cost (power and area) and significantly impacts communication performance in many-core architectures. Conventional topologies such as a 2D mesh have drawbacks including high diameter as the network scales and poor load balancing for the center nodes. We propose a methodology to design random topologies for on-chip networks. Random topologies provide better scalability in terms of network diameter and provide inherent load balancing. As a proof-of-concept for random on-chip topologies, we explore a novel set of networks -- do decs -- and illustrate how they reduce network diameter with randomized low-radix router connections. While a 4 × 4 mesh has a diameter of 6, our dodec has a diameter of 4 with lower cost. By introducing randomness, dodec networks exhibit more uniform message latency. By using low-radix routers, dodec networks simplify the router micro architecture and attain 20% area and 22% power reduction compared to mesh routers while delivering the same overall application performance for PARSEC.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".