Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations
Zhang Hu, Hongyang Pan, Yinshui Xia, Lunyao Wang, Zhufei Chu
Abstract
The independence of logic optimization and technology mapping poses a significant challenge in achieving high-quality synthesis results. Recent studies have improved optimization outcomes through collaborative optimization of multiple logic representations and have improved structural bias through structural choices. However, these methods still rely on technology-independent optimization and fail to truly resolve structural bias issues. This paper proposes a scalable and efficient framework based on Mixed Structural Choices (MCH). This is a novel heterogeneous mapping method that combines multiple logic representations with technology-aware optimization. MCH flexibly integrates different logic representations and stores candidates for various optimization strategies. By comprehensively evaluating the technology costs of these candidates, it enhances technology mapping and addresses structural bias issues in logic synthesis. Notably, the MCH-based lookup table (LUT) mapping algorithm set new records in the EPFL Best Results Challenge by combining the structural strengths of both And-Inverter Graph (AIG) and XOR-Majority Graph (XMG) logic representations. Additionally, MCH-based ASIC technology mapping achieves a area and delay reduction (balanced), 20.35% delay reduction (delay-oriented), and area reduction (area-oriented), outperforming traditional structural choice methods. Furthermore, MCH-based logic optimization utilizes diverse structures to surpass local optima and achieve better results.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 5a09a706-b929-4347-9e1b-73d6bd27b875Builds on3
- SLAP: A Supervised Learning Approach for Priority Cuts Technology MappingWalter Lau Neto, Matheus T. Moreira, Yingjie Li, Luca G. Amarù et al.DAC 2021 · 32 citations
- E-Syn: E-Graph Rewriting with Technology-Aware Cost Functions for Logic SynthesisChen Chen, Guangyu Hu, Dongsheng Zuo, Cunxi Yu et al.DAC 2024 · 17 citations
- Lightweight Structural Choices Operator for Technology MappingAntoine Grosnit, Matthieu Zimmer, Rasul Tutunov, Xing Li et al.DAC 2023 · 6 citations
Related papers
- Improving LUT-based optimization for ASICsWalter Lau Neto, Luca G. Amarù, Vinicius Possani, Patrick Vuillod et al.DAC 2022 · 6 citations
- E-morphic: Scalable Equality Saturation for Structural Exploration in Logic SynthesisChen Chen, Guangyu Hu, Cunxi Yu, Yuzhe Ma et al.DAC 2025 · 9 citations
- LUT-Based Optimization For ASIC Design FlowLuca Gaetano Amarù, Vinicius N. Possani, Eleonora Testa, Felipe S. Marranghello et al.DAC 2021 · 9 citations
- HIMap: a heuristic and iterative logic synthesis approachXing Li, Lei Chen, Fan Yang, Mingxuan Yuan et al.DAC 2022 · 11 citations
- Improving Standard-Cell Design Flow using Factored Form OptimizationAlessandro Tempia Calvino, Alan Mishchenko, Herman Schmit, Ethan Mahintorabi et al.DAC 2023 · 4 citations
