MacroMap: A technology mapping algorithm for heterogeneous FPGAs with effective area estimation
Bibliographic record
Abstract
Recent generation of FPGA devices takes advantage of speed and density benefits resulted from heterogeneous FPGA architecture, in which several basic LUTs can be combined to form one larger size LUT called Macro. Large Macros not only decrease network depth efficiently but also reduce area. In this paper, a new technology mapping algorithm, named MacroMap is proposed for the heterogeneous FPGAs with effective area estimation to overcome the main disadvantage that traditional technology mapping algorithms only generate one kind of typical K-LUT and cannot make full use of LUTs with different sizes (basic LUTs and Macros). Experimental results show that MacroMap can obtain 19% gain on area while keeping the network depth optimal compared with the existing heterogeneous FPGA mapping algorithm heteromap <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[8]</sup> .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".