“岂能尽如人意，但求无愧我心。”

“Life won’t always meet your expectation, but strive to live without hesitation.”

———《格言联璧·接物类》

Short Bio

I am a final year Ph.D. candidate at the Institute of Information Engineering, Chinese Academy of Sciences, co-advised by Prof. Rui Hou and Prof. Mingzhe Zhang.

In recent years, my research has focused on computer architecture, with a specialization in accelerator design for Fully Homomorphic Encryption (FHE) and the optimization of its associated applications. My work aims to advance the practical implementation of FHE, addressing critical challenges in data security and privacy.

Additionally, I am increasingly exploring accelerator/system design for Large Language Models (LLMs) to enhance their performance, efficiency and security.

News!

• 11/2025: One paper is accepted by ASPLOS 2026. Thanks to all collaborators!
• 10/2025: One paper is accepted by HPCA 2026. Thanks to all collaborators!
• 09/2025: I will join the Artifact Evaluation Committee of EuroSys 2025.
• 08/2025: I will join the Artifact Evaluation Committee of SOSP 2025.
• 07/2025: One paper is accepted by MICRO 2025. Thanks to all collaborators!
• 07/2025: I will join the Artifact Evaluation Committee of MICRO 2025.
• 06/2025: One paper is accepted by CCS 2025. Thanks to all collaborators!
• 04/2025: Two papers are accepted by ISCA 2025. Thanks to all collaborators!
• 12/2024: I will join the Artifact Evaluation Committee of HPCA 2025.
• 11/2024: One paper is accepted by HPCA 2025. Thanks to all collaborators!
• 07/2024: One paper is accepted by MICRO 2024. Thanks to all collaborators!
• 07/2023: One paper is accepted by TACO 2023. Thanks to all collaborators!
• 02/2023: Two papers are accepted by HPCA 2023. Thanks to all collaborators!

Publications

• Falcon: Algorithm-Hardware Co-Design for Efficient Fully Homomorphic Encryption Accelerator
  • Liang Kong, Xianglong Deng, Guang Fan, Shengyu Fan, Lei Chen, Yilan Zhu, Geng Yang, Yisong Chang, Shoumeng Yan, Mingzhe Zhang
• An Efficient and Scalable Hardware Architecture for Number Theoretic Transform on FPGA with Design Automation
  • Yilan Zhu, Geng Yang, Xingyu Tian, Dilshan Kumarathunga, Liang Kong, Xianglong Deng, Shengyu Fan, Guang Fan, Guiming Shi, Lei Chen, Bo Zhang, Yisong Chang, Shoumeng Yan, Zhenman Fang, Mingzhe Zhang
• WPC: Weight Plaintext Compression for CNN Inference based on RNS-CKKS
  • Guiming Shi, Yuchen Wei, Shengyu Fan, Xianglong Deng, Liang Kong, Xianbin Li, Jingwei Cai, Shuwen Deng, Mingzhe Zhang, Kaisheng Ma
• HAWK: Fully Homomorphic Encryption Accelerator with Fixed-Word Key Decomposition Switching
  • Liang Kong, Shengyu Fan, Xianglong Deng,Lei Chen, Guang Fan, Guiming Shi, Yilan Zhu, Geng Yang, Shoumeng, Mingzhe Zhang
• FAST:An FHE Accelerator for Scalable-parallelism with Tunable-bit
  • Shengyu Fan, Xianglong Deng, Liang Kong, Guiming Shi, Guang Fan, Dan Meng, Rui Hou, Mingzhe Zhang
• Neo: Towards Efficient Fully Homomorphic Encryption Acceleration using Tensor Core
  • Dian Jiao, Xianglong Deng, Zhiwei Wang, Shengyu Fan, Dan Meng, Rui Hou, Mingzhe Zhang
• WarpDrive: GPU-Based Fully Homomorphic Encryption Acceleration Leveraging Tensor and CUDA Cores
  • Guang Fan, Mingzhe Zhang, Fangyu Zheng, Shengyu Fan, Tian Zhou, Xianglong Deng, Wenxu Tang, Liang Kong, Yixuan Song, and Shoumeng Yan
• Trinity: A General Purpose FHE Accelerator
  • Xianglong Deng^*, Shengyu Fan^*, Zhicheng Hu, Zhuoyu Tian, Zihao Yang, Jiangrui Yu, Dingyuan Cao, Dan Meng, Rui Hou, Meng Li, Liang Chang, Qian Lou, Mingzhe Zhang
  • ^*The authors contribute equally.
• Accelerating convolutional neural network by exploiting sparsity on gpus
  • Weizhi Xu, Yintai Sun, Shengyu Fan, Hui Yu, Xin Fu
• TensorFHE: Achieving Practical Computation on Encrypted Data Using GPGPU
  • Shengyu Fan, Zhiwei Wang, Weizhi Xu, Rui Hou, Dan Meng, Mingzhe Zhang
• Poseidon: Practical Homomorphic Encryption Accelerator
  • Yinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu, Mingzhe Zhang, Xiaowei Li