Towards interconnect-adaptive packing for FPGAs
Why this work is in the frame
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Bibliographic record
Abstract
In order to investigate new FPGA logic blocks, FPGA architects have traditionally needed to customize CAD tools to make use of the new features and characteristics of those blocks. The software development effort necessary to create such CAD tools can be a time-consuming process that can significantly limit the number and variety of architectures explored. Thus, architects want flexible CAD tools that can, with few or no software modifications, explore a diverse space. Existing flexible CAD tools suffer from impractically long runtimes and/or fail to efficiently make use of the important new features of the logic blocks being investigated. This work is a step towards addressing these concerns by enhancing the packing stage of the open-source VTR CAD flow [17] to efficiently deal with common interconnect structures that are used to create many kinds of useful novel blocks. These structures include crossbars, carry chains, dedicated signals, and others. To accomplish this, we employ three techniques in this work: speculative packing, pre-packing, and interconnect-aware pin counting. We show that these techniques, along with three minor modifications, result in improvements to runtime and quality of results across a spectrum of architectures, while simultaneously expanding the scope of architectures that can be explored. Compared with VTR 1.0 [17], we show an average 12-fold speedup in packing for fracturable LUT architectures with 20% lower minimum channel width and 6% lower critical path delay. We obtain a 6 to 7-fold speedup for architectures with non-fracturable LUTs and architectures with depopulated crossbars. In addition, we demonstrate packing support for logic blocks with carry chains.
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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 it