arXiv subjects

Yalun Cai

Publications and source records attributed to Yalun Cai.

4 recordsLinked to original sources

arXiv2026

Rethinking Clause Management for CDCL SAT Solvers

Boolean Satisfiability (SAT) solving underpins a wide range of applications in Electronic Design Automation (EDA), particularly formal verification. However, this paper observes that the mainstream clause reduction heuristic in modern SAT solvers becomes ineffective in the critical domain of complex arithmetic circuit verification, such as multipliers. On these instances, the dominant Literal Block Distance (LBD) metric for measuring clause quality degrades into a simple value of clause length, without any perception of dynamic clause usage during solving. To address this issue, a novel clause reduction mechanism is proposed, which is entirely independent of LBD. Its core idea is to decouple and handle separately the two most fundamental characteristics of learnt clauses--inherent lineage and dynamic usage patterns--thereby avoiding the efficiency degradation caused by inappropriately mixing these properties. Experiments show that our method consistently improves mainstream solvers and achieves speedups of up to 5.74x on complex arithmetic circuit problems, while maintaining comparable performance on general-purpose benchmarks. These results challenge the prevailing LBD-centric clause quality metric for clause management.

cs.LO↗

arXiv2025

Circuit-Aware SAT Solving: Guiding CDCL via Conditional Probabilities

Circuit Satisfiability (CSAT) plays a pivotal role in Electronic Design Automation. The standard workflow for solving CSAT problems converts circuits into Conjunctive Normal Form (CNF) and employs generic SAT solvers powered by Conflict-Driven Clause Learning (CDCL). However, this process inherently discards rich structural and functional information, leading to suboptimal solver performance. To address this limitation, we introduce CASCAD, a novel circuit-aware SAT solving framework that directly leverages circuit-level conditional probabilities computed via Graph Neural Networks (GNNs). By explicitly modeling gate-level conditional probabilities, CASCAD dynamically guides two critical CDCL heuristics -- variable phase selection and clause managementto significantly enhance solver efficiency. Extensive evaluations on challenging real-world Logical Equivalence Checking (LEC) benchmarks demonstrate that CASCAD reduces solving times by up to 10x compared to state-of-the-art CNF-based approaches, achieving an additional 23.5% runtime reduction via our probability-guided clause filtering strategy. Our results underscore the importance of preserving circuit-level structural insights within SAT solvers, providing a robust foundation for future improvements in SAT-solving efficiency and EDA tool design.

cs.AI↗

arXiv2024

CuckooGraph: A Scalable and Space-Time Efficient Data Structure for Large-Scale Dynamic Graphs

Graphs play an increasingly important role in various big data applications. However, existing graph data structures cannot simultaneously address the performance bottlenecks caused by the dynamic updates, large scale, and high query complexity of current graphs. This paper proposes a novel data structure for large-scale dynamic graphs called CuckooGraph. It does not require any prior knowledge of the upcoming graphs, and can adaptively resize to the most memory-efficient form while requiring few memory accesses for very fast graph data processing. The key techniques of CuckooGraph include TRANSFORMATION and DENYLIST. TRANSFORMATION fully utilizes the limited memory by designing related data structures that allow flexible space transformations to smoothly expand/tighten the required space depending on the number of incoming items. DENYLIST efficiently handles item insertion failures and further improves processing speed. Our experimental results show that compared with the most competitive solution Spruce, CuckooGraph achieves about $33\times$ higher insertion throughput while requiring only about $68\%$ of the memory space.

cs.DB↗

arXiv2022

Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks

The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3\% classification accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN.

cond-mat.mtrl-sci↗