GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration. - Stefan Abi-Karam; Yuqi He; Rishov Sarkar; Lakshmi Sathidevi; Zihang Qiao; Cong Hao (CoRR, 2022)

FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming. - Rishov Sarkar; Stefan Abi-Karam; Yuqi He; Lakshmi Sathidevi; Cong Hao (CoRR, 2022)

M5: Multi-modal Multi-task Model Mapping on Multi-FPGA with Accelerator Configuration Search. - Akshay Karkal Kamath; Stefan Abi-Karam; Ashwin Bhat; Cong Hao (DATE, 2023)

FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference. - Rishov Sarkar; Stefan Abi-Karam; Yuqi He; Lakshmi Sathidevi; Cong Hao (HPCA, 2023)

INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing. - Stefan Abi-Karam; Rishov Sarkar; Dejia Xu; Zhiwen Fan; Zhangyang Wang; Cong Hao (ICCAD, 2023)

GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization. - Stefan Abi-Karam; Cong Hao (Softw. Pract. Exp., 2023)

HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond. - Stefan Abi-Karam; Rishov Sarkar; Allison Seigler; Sean Lowe; Zhigang Wei; Hanqiu Chen; Nanditha Rao; Lizy Kurian John; Aman Arora 001; Cong Hao (MLCAD, 2024)

HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks. - Stefan Abi-Karam; Cong Hao (CoRR, 2025)