24 citations · 39 across the 12 of their papers we have counts for
6 papers · 1 filter
Report for NSF Workshop on AI for Electronic Design Automation
Deming Chen, Vijay Ganesh, Weikai Li +7
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIP…
Iceberg: Enhancing HLS Modeling with Synthetic Data
Zijian Ding, Tung Nguyen, Weikai Li +3
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap…
Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
Weikai Li, Ding Wang, Zijian Ding +4
High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the sou…
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…
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…
Rethinking the Setting of Semi-supervised Learning on Graphs
Ziang Li, Ming Ding, Weikai Li +4
We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In th…