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20242026
most citedReport for NSF Workshop on AI for Electronic Design Automation

2 citations · 3 across the 12 of their papers we have counts for

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cs.LG20262 cited

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…

cs.LG2025

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…

cs.LG2025

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun +1

FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising…

cs.LG20241 cited

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…

cs.LG2024

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…