Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations

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 $3.73 \%$ area and $8.94 \%$ delay reduction (balanced), 20.35% delay reduction (delay-oriented), and $\mathbf{2 1. 0 2 \%}$ 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.

Authors

Institutions

Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133368
Citations
1
Primary Topic
Technology Assessment and Management
Type
article
Field-Weighted Citation Impact
1.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations

Hu Zhang, Zhufei Chu, Lunyao Wang, Yinshui Xia et al.
1 citations
Technology Assessment and Management
1.30
article

Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations

Hu Zhang, Zhufei Chu, Lunyao Wang, Yinshui Xia, Hongyang Pan
article en
1 citations

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 $3.73 \%$ area and $8.94 \%$ delay reduction (balanced), 20.35% delay reduction (delay-oriented), and $\mathbf{2 1. 0 2 \%}$ 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.

Ningbo University (CN), Shanghai Fudan Microelectronics (China) (CN)
Openalex Percentile: Top 17%
Technology Assessment and Management
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations — Hu Zhang, Zhufei Chu, et al. · (2025) | TGRS Research Map | TGRS