Improving Digital Circuit Simulation with Batch-Parallel Logic Evaluation
Bibliographic record
Abstract
Integrated circuit simulators reproduce the behavior and functionality of the underlying circuits. They are part of FPGA CAD flow tools and they ensure the correctness of the circuits after the various conversions and optimizations occurring in the previous stages. During this procedure a graph with dependencies across nodes is created for each circuit design. Large circuits, and thus graphs, require more time to be simulated, making a parallel approach necessary. We explore a new solution-batch-parallel simulation in which the circuit output is calculated by worker threads that process batches of input vectors. The threads traverse and calculate their assigned nodes in parallel taking into consideration the intra-node dependencies. Furthermore, a node calculation analysis is performed and used to achieve work balance across threads. We apply this technique on the open-source Odin II framework and compare it with the existing approaches. The batch-parallel simulation is compared with the two existing approaches, single-threaded and multi-threaded, under various configurations, considering the number of threads and the batch sizes. The results demonstrate performance gains against the existing approaches in the majority of the benchmarks used for specific metrics, such as simulation elapsed time.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".