4 citations · 7 across the 6 of their papers we have counts for
6 papers
Open-Set Living Need Prediction with Large Language Models
Xiaochong Lan, Jie Feng, Yizhou Sun +5
Living needs are the needs people generate in their daily lives for survival and well-being. On life service platforms like Meituan, user purchases are driven by living needs, maki…
Learning to Compare Hardware Designs for High-Level Synthesis
Yunsheng Bai, Atefeh Sohrabizadeh, Zijian Ding +6
High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs, enabling the rapid development of hardware accelerators. HLS relies…
Efficient Task Transfer for HLS DSE
Zijian Ding, Atefeh Sohrabizadeh, Weikai Li +3
There have been several recent works proposed to utilize model-based optimization methods to improve the productivity of using high-level synthesis (HLS) to design domain-specific…
Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis
Zongyue Qin, Yunsheng Bai, Atefeh Sohrabizadeh +4
In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers us…
Fast Inference of Removal-Based Node Influence
Weikai Li, Zhiping Xiao, Xiao Luo +1
Graph neural networks (GNNs) are widely utilized to capture the information spreading patterns in graphs. While remarkable performance has been achieved, there is a new trending to…
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
Shichang Zhang, Jiani Zhang, Xiang Song +4
Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and he…