VIPER: A VTR Interface for Placement with Error Resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The open source Verilog-to-Routing (VTR) tool flow can produce legal placement solutions for arbitrarily complex FPGA architectures and is thus widely used for novel device development as well as CAD tool research. VTR’s versatility is enabled by both its robust device modeling capability and its use of pre-placement clustering to abstract away complexity and maintain scalability. Clustering is not always necessary; recent academic tools demonstrate that delaying or omitting it can improve result quality for some device architectures. By incorporating a variety of external placement tools, VTR can maintain both versatility and result quality as FPGAs scale and diversify. Tool developers can benefit as well from access to VTR; however, due to VTR’s complex, hierarchical device modeling, its place and route interface requires a level of detail and accuracy that is beyond the scope of many external tools. To lower the barrier to interoperability, we introduce a VTR Interface for Placement with Error Resilience (VIPER). VIPER constructs a complete, legal, VTR-compatible clustering and placement solution based on a simplified and potentially illegal input placement.
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.
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 | 0.001 | 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