Efficient quadratic placement for FPGAs.
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGAs) are widely used in industry because they can implement any digital circuit on site simply by specifying programmable logic and their interconnections. However, this rapid prototyping advantage may be adversely affected because of the long compile time, which is dominated by placement and routing. This issue is of great importance, especially as the logic capacities of FPGAs continue to grow. This thesis focuses on the placement phase of FPGA Computer Aided Design (CAD) flow and presents a fast, high quality, wirelength-driven placement algorithm for FPGAs that is based on the quadratic placement approach. In this thesis, multiple iterations of equation solving process together with a linear wirelength reduction technique are introduced. The proposed algorithm efficiently handles the main problems with the quadratic placement algorithm and produces a fast and high quality placement. Experimental results, using twenty benchmark circuits, show that this algorithm can achieve comparable total wirelength and, on average, 5X faster run time when compared to an existing, state-of-the-art placement tool. This thesis also shows that the proposed algorithm delivers promising preliminary results in minimizing the critical path delay while maintaining high placement quality.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .X86. Source: Masters Abstracts International, Volume: 44-04, page: 1946. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".