DAG-aware Synthesis Orchestration
Not on the map. Why?
Abstract (arXiv)
The key methodologies of modern logic synthesis techniques are conducted on multi-level technology-independent representations such as And-Inverter-Graphs (AIGs) of the digital logic via directed-acyclic-graph (DAGs) traversal based structural rewriting, resubstitution, and refactoring. Existing state-of-the-art DAG-aware logic synthesis algorithms are all designed to perform stand-alone optimizations during a single DAG traversal. However, we empirically identify and demonstrate that these algorithms are limited in quality-of-results and runtime complexity due to this design concept. This work proposes Synthesis Orchestration, which orchestrates stand-alone operations within the single traversal of AIG. Thus, orchestration method explores more optimization opportunities and results in better performance. Our experimental results are comprehensively conducted on all 104 designs collected from ISCAS'85/89/99, VTR, and EPFL benchmark suites, with consistent logic minimization improvements over rewriting, resubstitution, refactoring, leading to an average of 4% more node reduction with improved runtime efficiency for the single optimization. Moreover, we evaluate orchestration as a plug-in algorithm in resyn and resyn3 flows in ABC, which demonstrates consistent logic minimization improvements (3.8% and 10.9% more node reduction on average). The runtime analysis demonstrates the orchestration outperforms stand-alone algorithms in both AIG minimization and runtime efficiency. Finally, we integrate the orchestration into OpenROAD for end-to-end performance evaluation. Our results demonstrate the advantages of the orchestration optimization technique, even after technology mapping and post-routing in the design flow have been conducted.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2024BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulationcited by 2
- 2025e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solvingcited by 2
- 2026Learning Compact Boolean Networkscited by 1
- 2026Rethinking Logic Optimization Operators: Theory-Derived Operator Compression via Agentic Source Analysiscited by 1
- 2025Revisit Choice Network for Synthesis and Technology Mappingcited by 0
- 2026Rethinking Logic Optimization Operators: Theory-Derived Operator Compression via Agentic Source Analysis26 Jul 2026
- 2026Learning Compact Boolean Networks5 Feb 2026
- 2025e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving18 Aug 2025
- 2025Revisit Choice Network for Synthesis and Technology Mapping4 Aug 2025
- 2024BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation19 Jan 2024