5 papers
Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture
Xiang Li, Shanshan Wang, Chenglong Xiao
The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorit…
SoberDSE: Sample-Efficient Design Space Exploration via Learning-Based Algorithm Selection
Lei Xu, Shanshan Wang, Chenglong Xiao
High-Level Synthesis (HLS) is a pivotal electronic design automation (EDA) technology that enables the generation of hardware circuits from high-level language descriptions. A crit…
MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Exploration
Lei Xu, Shanshan Wang, Chenglong Xiao
High-Level Synthesis (HLS) design space exploration (DSE) seeks Pareto-optimal designs within expansive pragma configuration spaces. To accelerate HLS DSE, graph neural networks (G…
Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models
Lei Xu, Shanshan Wang, Emmanuel Casseau +1
High-Level Synthesis (HLS) Design Space Exploration (DSE) is essential for generating hardware designs that balance performance, power, and area (PPA). To optimize this process, ex…
An algorithm with a delay of for enumerating connected induced subgraphs of size
Chenglong Xiao, Chengyong Mao, Shanshan Wang
The problem of enumerating connected subgraphs of a given size in a graph has been extensively studied in recent years. In this paper, we propose an algorithm with a delay of $O(kÎ…