Automatic and Simultaneous Floorplanning and Placement in Field-Programmable Gate Arrays With Dynamic Partial Reconfiguration Based on Genetic Algorithm
Bibliographic record
Abstract
Using dynamic partial reconfiguration (DPR) feature in field-programmable gate array (FPGA) systems seems inevitable by considering the tremendous benefits, such as reduced cost and power. Nowadays, manual floorplanning is one of the difficulties in implementing DPR systems, which relies on the designer’s views and his command over designing the concepts for arranging the modules on the physical layout of the FPGA more efficiently, as the results of floorplanning can influence the next stages, such as the placement. In other words, placement and floorplanning that are separately conducted in the today’s tools are interdependent and the floorplanning results play a role in the placement and vice versa. This article aimed to propose a method for conducting floorplanning and placement simultaneously in DPR systems according to the genetic algorithm (GA). The proposed algorithm was tested on 20 largest MCNC benchmark circuits with DPR-support capability. Based on the results, wirelength and critical path delay improved by 14% and 17%, respectively, compared with Xilinx’s early access partial reconfiguration design flow (EAPR). However, area and runtime increased by about 2% and 8%, respectively. The proposed method was also compared with other research that uses B* tree and simulated annealing algorithm. The results showed that our proposed algorithm is competitive in various parameters with other research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".