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I am a fifth year Ph.D. candidate in the Ming Hsieh Department of Electrical and Computer Engineering at University of Southern California. In Fall 2016, I joined the FPGA/Parallel Computing lab under the supervision of Prof. Viktor Prasanna. Before graduate study, I obtained my Bachelor's degree in electronic engineering from University of Hong Kong.
My general research interest lies in solving memory and computation intensive problems by innovative algorithm-architecture mapping approaches. My current focus is on improving the scalability of graph representation learning. I have developed sampling methods to efficiently and accurately compute Graph Neural Network (GNN), especially for deep models and large graphs. I have also developed various GNN/CNN accelerators by parallelization on heterogeneous platforms (GPU, CPU and FPGA).
News
Sept 2021: Full paper shaDow-GNN accepted at NeurIPS 2021!
May 2021: Mentoring session at ICLR 2021. Feel free to chat!
May 2021: Starting summer internship at Facebook AI Applied Research (FAIAR)
April 2021: Full paper GCNP accepted at VLDB 2021!
May 2020: Starting summer internship at FAIAR
Feb 2020: Presented our work GraphACT at the ACM/FPGA 2020 conference
Dec 2019: Full paper GraphSAINT accepted at ICLR 2020!
Software Releases
GraphSAINT: Graph Sampling Based Inductive Learning Method (ICLR 2020)
shaDow-GNN: Decoupling the Depth and Scope of Graph Neural Networks (NeurIPS 2021)
Services
Reviewer ICLR 2022, NeurIPS 2021, ICLR 2021 (awarded outstanding reviewer); PC member IJCAI 2020;
Reviewer IEEE transactions 10+ times;
Publications
Large Scale Graph Representation Learning
[Full paper] Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen. Decoupling the Depth and Scope of Graph Neural Networks. In NeurIPS, 2021.
[Full paper] Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, and Viktor Prasanna. Accelerating Large Scale Real-Time GNN Inference using Channel Pruning. In Proceddings of the VLDB Endowment (PVLDB), 2021.
[Journal] Hanqing Zeng*, Hongkuan Zhou*, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Accurate, Efficient and Scalable Training of Graph Neural Networks. In Journal of Parallel and Distributed Computing, 2021.
[Full paper] Bingyi Zhang, Hanqing Zeng, and Viktor Prasanna. Hardware Acceleration of Large Scale GCN Inference. In the 31st IEEE International Conference on Application-specific Systems, Architectures and Processors, 2020.
[Poster] Bingyi Zhang, Hanqing Zeng, and Viktor Prasanna. Accelerating Large Scale GCN Inference on FPGA. In 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2020
[Full paper] Hanqing Zeng*, Hongkuan Zhou*, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. GraphSAINT: Graph sampling based inductive learning method. In International Conference on Learning Representations (ICLR), 2020.
[Full paper] Hanqing Zeng, and Viktor Prasanna. GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms. In Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '20. ACM, 2020.
[Full paper] Hanqing Zeng*, Hongkuan Zhou*, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Accurate, efficient and scalable graph embedding. In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2019.
Accelerating Convolutional Neural Network Computation
[Full paper] Yue Niu, Hanqing Zeng, Ajitesh Srivastava, Kartik Lakhotia, Rajgopal Kannan, Yanzhi Wang, and Viktor Prasanna. SPEC2: Spectral Sparse CNN Accelerator on FPGAs. In 2019 IEEE 26th International Conference on High Performance Computing (HiPC), 2019.
[Full paper] Rachit Rajat*, Hanqing Zeng*, and Viktor Prasanna. A flexible design automation tool for accelerating quantized spectral CNNs. In 2019 29th International Conference on Field Programmable Logic and Applications (FPL), 2019.
[Full paper] Weiyi Sun, Hanqing Zeng, Yi-hua Edward Yang, and Viktor Prasanna. Throughput-optimized frequency domain CNN with fixed-point quantization on FPGA. In 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 2018.
[Full paper] Hanqing Zeng, Ren Chen, Chi Zhang, and Viktor Prasanna. A framework for generating high throughput CNN implementations on FPGAs. In Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '18. ACM, 2018.
[Full paper] Hanqing Zeng, Chi Zhang, and Viktor Prasanna. Fast generation of high throughput customized deep learning accelerators on FPGAs. In 2017 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 2017.
Parallel Graph Processing
[Full paper] Qing Dong, Kartik Lakhotia, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna, and Guna Seetharaman. A fast and efficient parallel algorithm for pruned landmark labeling. In 2018 IEEE High Performance extreme Computing Conference (HPEC), 2018.
[Full paper] Shijie Zhou, Rajgopal Kannan, Hanqing Zeng, and Viktor Prasanna. An FPGA framework for edge-centric graph processing. In Proceedings of the 15th ACM International Conference on Computing Frontiers, 2018.
[Full paper] Shijie Zhou, Kartik Lakhotia, Shreyas Singapura, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna, James Fox, Euna Kim, Oded Green, and David Bader. Design and implementation of parallel PageRank on multicore platforms. In 2017 IEEE High Performance extreme Computing Conference (HPEC), 2017.
pdf Student Innovation Award
[Full paper] Oded Green, James Fox, Euna Kim, Federico Busato, Nicola Bombieri, Kartik Lakhotia, Shijie Zhou, Shreyas Singapura, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna, and David Bader. Quickly finding a truss in a haystack. In 2017 IEEE High Performance extreme Computing Conference (HPEC), 2017.
pdf Innovation Award