26 citations · 119 across the 19 of their papers we have counts for
20 papers
Logic Optimization Meets SAT: A Novel Framework for Circuit-SAT Solving
Zhengyuan Shi, Tiebing Tang, Jiaying Zhu +5
The Circuit Satisfiability (CSAT) problem, a variant of the Boolean Satisfiability (SAT) problem, plays a critical role in integrated circuit design and verification. However, exis…
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer
Yu Zhang, Hui-Ling Zhen, Zehua Pei +4
Considering the challenges faced by large language models (LLMs) in logical reasoning and planning, prior efforts have sought to augment LLMs with access to external solvers. While…
BetterV: Controlled Verilog Generation with Discriminative Guidance
Zehua Pei, Hui-Ling Zhen, Mingxuan Yuan +2
Due to the growing complexity of modern Integrated Circuits (ICs), there is a need for automated circuit design methods. Recent years have seen rising research in hardware design l…
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li, Fangzhou Zhu, Hui-Ling Zhen +23
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the inte…
LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation
Ruizhe Zhong, Xingbo Du, Shixiong Kai +7
Driven by Moore's Law, the complexity and scale of modern chip design are increasing rapidly. Electronic Design Automation (EDA) has been widely applied to address the challenges e…
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Zhihai Wang, Lei Chen, Jie Wang +7
Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of the semiconductor industry. A key task in LS is to transform circuits -- modeled by directed acyclic grap…
DeepGate2: Functionality-Aware Circuit Representation Learning
Zhengyuan Shi, Hongyang Pan, Sadaf Khan +7
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and l…
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
Yiyuan Yang, Rongshang Li, Qiquan Shi +4
Data prefetching is important for storage system optimization and access performance improvement. Traditional prefetchers work well for mining access patterns of sequential logical…
Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG
Hui-Ling Zhen, Naixing Wang, Junhua Huang +3
Due to the increasing challenges posed by the relentless rise in the design complexity of integrated circuits, Boolean Satisfiability (SAT) has emerged as a robust alternative to s…
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Fei Liu, Chengyu Lu, Lin Gui +3
Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a s…
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
Yang Li, Xinyan Chen, Wenxuan Guo +6
Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation approaches can hardly simultaneously capture the global structural properties and main…
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Zhihai Wang, Xijun Li, Jie Wang +5
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection -- which aim…
SATformer: Transformer-Based UNSAT Core Learning
Zhengyuan Shi, Min Li, Yi Liu +5
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the…
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Zeren Huang, Wenhao Chen, Weinan Zhang +7
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected…
LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems
Ji Zhang, Xijun Li, Xiyao Zhou +4
Cache plays an important role to maintain high and stable performance (i.e. high throughput, low tail latency and throughput jitter) in storage systems. Existing rule-based cache m…
A Survey for Solving Mixed Integer Programming via Machine Learning
Jiayi Zhang, Chang Liu, Junchi Yan +3
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem, and most of the combinatori…
Machine Learning Methods in Solving the Boolean Satisfiability Problem
Wenxuan Guo, Junchi Yan, Hui-Ling Zhen +3
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despi…
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry
Jiahui Duan, Xialiang Tong, Fei Ni +3
The bin packing problem exists widely in real logistic scenarios (e.g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transpor…
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
Jianye Hao, Jiawen Lu, Xijun Li +4
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain. So far, research on this problem has mainly focused on using artificial data whi…
Learning to Select Cuts for Efficient Mixed-Integer Programming
Zeren Huang, Kerong Wang, Furui Liu +6
Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in…