Optimization of Transistor-Level Floorplans for Field-Programmable Gate Arrays. Bachelor’s Thesis
Bibliographic record
Abstract
The design and custom hand-layout of FPGAs (Field-Programmable Gate Arrays) is a painstaking process that takes many person-years of effort to complete. This research builds upon groundbreaking work done at the University of Toronto to work towards the construction of a tool that automatically generates physical layouts from FPGA architectural specifications. In particular, this research focuses on improving the performance of the placement phase of the layout generation engine of that tool. Various heuristics, some of which make use of specific knowledge of FPGA circuitry, were developed to reduce the area and the amount of wiring resources needed to connect the functional cells within an FPGA layout. Comparisons with the original version, based on practical FPGA architectures, demonstrate the improved layout engine produces layouts about 40 % smaller, on average, with about 30 % less wiring demand. ii ACKNOWLEDGEMENTS I would like to thank my supervisor, Jonathan Rose, for the guidance he offered and the general wisdom he shared. His enthusiasm is as remarkable as his vision. I would also like to thank Ketan Padalia, the developer of the tools that serve the
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.008 | 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".