End-to-End Optimization of Deep Learning Applications. - Atefeh Sohrabizadeh; Jie Wang 022; Jason Cong (FPGA, 2020)

AutoDSE: Enabling Software Programmers Design Efficient FPGA Accelerators. - Atefeh Sohrabizadeh; Cody Hao Yu; Min Gao 003; Jason Cong (FPGA, 2021)

SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation. - Atefeh Sohrabizadeh; Yuze Chi; Jason Cong (CoRR, 2021)

Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks. - Atefeh Sohrabizadeh; Yunsheng Bai; Yizhou Sun; Jason Cong (CoRR, 2021)

StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing. - Atefeh Sohrabizadeh; Yuze Chi; Jason Cong (CICC, 2022)

Improving GNN-based accelerator design automation with meta learning. - Yunsheng Bai; Atefeh Sohrabizadeh; Yizhou Sun; Jason Cong (DAC, 2022)

Automated accelerator optimization aided by graph neural networks. - Atefeh Sohrabizadeh; Yunsheng Bai; Yizhou Sun; Jason Cong (DAC, 2022)

A Versatile Systolic Array for Transposed and Dilated Convolution on FPGA. - Suhail Basalama; Atefeh Sohrabizadeh; Jie Wang 022; Jason Cong (FCCM, 2022)

SPA-GCN: Efficient and Flexible GCN Accelerator with Application for Graph Similarity Computation. - Atefeh Sohrabizadeh; Yuze Chi; Jason Cong (FPGA, 2022)

Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication. - Linghao Song; Yuze Chi; Atefeh Sohrabizadeh; Young-kyu Choi; Jason Lau; Jason Cong (FPGA, 2022)

OverGen: Improving FPGA Usability through Domain-specific Overlay Generation. - Sihao Liu; Jian Weng 002; Dylan Kupsh; Atefeh Sohrabizadeh; Zhengrong Wang; Licheng Guo; Jiuyang Liu; Maxim Zhulin; Rishabh Mani; Lucheng Zhang; Jason Cong; Tony Nowatzki (MICRO, 2022)

AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators. - Atefeh Sohrabizadeh; Cody Hao Yu; Min Gao 003; Jason Cong (ACM Trans. Design Autom. Electr. Syst., 2022)

Robust GNN-Based Representation Learning for HLS. - Atefeh Sohrabizadeh; Yunsheng Bai; Yizhou Sun; Jason Cong (ICCAD, 2023)

Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs. - Yunsheng Bai; Atefeh Sohrabizadeh; Zongyue Qin; Ziniu Hu; Yizhou Sun; Jason Cong (NeurIPS, 2023)

Democratizing Domain-Specific Computing. - Yuze Chi; Weikang Qiao; Atefeh Sohrabizadeh; Jie Wang 022; Jason Cong (Commun. ACM, 2023)

A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware. - Shichang Zhang; Atefeh Sohrabizadeh; Cheng Wan 005; Zijie Huang 002; Ziniu Hu; Yewen Wang; Yingyan Lin; Jason Cong; Yizhou Sun (CoRR, 2023)

FlexCNN: An End-to-end Framework for Composing CNN Accelerators on FPGA. - Suhail Basalama; Atefeh Sohrabizadeh; Jie Wang 022; Licheng Guo; Jason Cong (ACM Trans. Reconfigurable Technol. Syst., 2023)

Efficient Task Transfer for HLS DSE. - Zijian Ding; Atefeh Sohrabizadeh; Weikai Li 002; Zongyue Qin; Yizhou Sun; Jason Cong (ICCAD, 2024)

GNN-Based Performance Prediction of Quantum Optimization of Maximum Independent Set. - Atefeh Sohrabizadeh; Wan-Hsuan Lin; Daniel Bochen Tan; Madelyn Cain; Sheng-Tao Wang; Mikhail D. Lukin; Jason Cong (ICCAD, 2024)

Learning to Compare Hardware Designs for High-Level Synthesis. - Yunsheng Bai; Atefeh Sohrabizadeh; Zijian Ding; Rongjian Liang; Weikai Li 002; Ding Wang; Haoxing Ren; Yizhou Sun; Jason Cong (MLCAD, 2024)

Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis. - Zongyue Qin; Yunsheng Bai; Atefeh Sohrabizadeh; Zijian Ding; Ziniu Hu; Yizhou Sun; Jason Cong (MLCAD, 2024)

Customized Computing and Machine Learning - Atefeh Sohrabizadeh (2024)

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis. - Weikai Li 002; Ding Wang; Zijian Ding; Atefeh Sohrabizadeh; Zongyue Qin; Jason Cong; Yizhou Sun (AAAI, 2025)