Automatic detection and elision of reset sub-circuits
Bibliographic record
Abstract
Electronic circuits are too complex to be designed by hand so hardware languages, like Verilog, and Computer Aided Design (CAD) tools are used for these purposes. Two main types of circuits are Application-Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs). ASICs require a reset sub-circuit to initialize their state; however, such a procedure is not necessary for FPGAs that support power-on reset. We propose and evaluate a tool that automatically detects and elides reset sub-circuits as part of the Verilog-to-Routing (VTR) CAD flow and in particular Odin II. Also, our tool can be used to decide if a reset sub-circuit has been properly implemented and can point towards memory components that have not been initialized. Such a tool is the first to our knowledge. Our tests with the VTR Verilog benchmarks and other Verilog circuits showed significant reductions in resource consumption on the target FPGAs as much as 25.9% shorter critical path, 90.39% shorter maximum net, 62.84% fewer used logic blocks and 30.87% fewer used routing elements. Also, our approach yielded significant reductions in the execution time of the placement-and-routing algorithm for the elided circuits that were as high as 4.5 times faster VPR execution and 3.35 times faster overall VTR flow execution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".