VPR 5.0
Bibliographic record
Abstract
The VPR toolset has been widely used in FPGA architecture and CAD research, but has not evolved over the past decade. This article describes and illustrates the use of a new version of the toolset that includes four new features: first, it supports a broad range of single-driver routing architectures, which have superior architectural and electrical properties over the prior multidriver approach (and which is now employed in the majority of FPGAs sold). Second, it can now model, for placement and routing a heterogeneous selection of hard logic blocks. This is a key (but not final) step toward the incluion of blocks such as memory and multipliers. Third, we provide optimized electrical models for a wide range of architectures in different process technologies, including a range of area-delay trade-offs for each single architecture. Finally, to maintain robustness and support future development the release includes a set of regression tests for the software. To illustrate the use of the new features, we explore several architectural issues: the FPGA area efficiency versus logic block granularity, the effect of single-driver routing, and a simple use of the heterogeneity to explore the impact of hard multipliers on wiring track count.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.036 |
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".