Advanced And-Inverter Graph Decomposition Technique for Reducing Circuit Complexity
Bibliographic record
Abstract
In the field of Electronic Design Automation (EDA), managing circuit complexity is a crucial task for efficient circuit verification, testing, and optimization. Increasing design complexity presents challenges for tasks such as formal verification, fault detection, and circuit optimization. Therefore, reducing circuit complexity becomes crucial in improving the efficiency and scalability of these tasks. These circuits are typically represented as graphs. In the field of parameterized complexity, CutWidth (CW) and TreeWidth (TW) are well-studied decomposition techniques that have been used in analyzing graph algorithms. In this paper, we introduce the TW decomposition technique to the field of EDA for the first time and demonstrate its impact on reducing the circuit complexity of circuits. Additionally, we present a new decomposition technique that combines both decompositions, resulting in a further reduction in circuit complexity. Furthermore, we present experimental results comparing complexity upper bounds from various decompositions to highlight the efficacy of our approach on the ISCAS’85 and EPFL benchmark circuits. Our results show that our decomposition technique outperforms the complexity upper bounds of CW by 90.16× and the complexity upper bounds of TW by 9.34× for the ISCAS’85 benchmarks. Additionally, it outperforms the complexity upper bounds of CW by 1986.37× and the complexity upper bounds of TW by 94.13× for the EPFL benchmarks. Finally, to demonstrate the applicability of the decomposition techniques in solving various EDA problems, we propose a new Formal Verification (FV) approach that leverages these techniques to provide an upper bound for the verification process. We also conduct an experimental evaluation on the ITC’99 , MCNC’91 , and VHDL Library of Arithmetic Units ( ELAU ) benchmark circuits, adder circuits of various sizes (up to 3072-bit width), and Genmul multipliers of different sizes (up to 10×10), to demonstrate the scalability of our approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".