A comparison of hardware acceleration methods for VLSI Maze routing
Bibliographic record
Abstract
One of most popular algorithms for finding a path between any two pins on a planar graph is Lee's algorithm. In this paper, three different approaches are proposed and investigated for accelerating Lee's algorithm. The first approach is based on a hardware/software co-design strategy, while the second is a custom hardware implementation using Handel-C. An application specific instruction implementation is also implemented and investigated. This approach targets the Tensilica configurable processor. The experimental results show that the three approaches produce the same quality solutions as the pure-software implementation. However, the co-design approach achieves an average speedup of 4.3× over the pure-software based approach, while the custom hardware approach achieves an average speed up of 3.9×. The configurable approach obtained an average speedup of 33.6× over the pure software, while achieving a speedup of 7.81× and 8.61× over the hardware/software co-design and the custom hardware respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".