Poseidon: Practical Homomorphic Encryption Accelerator
Yinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu, Mingzhe Zhang, Xiaowei Li
摘要
With the development of the important solution for privacy computing, the explosion of data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to the hardware design. In this paper, we propose a practical FHE accelerator - "Poseidon", which focuses on improving the hardware resource and bandwidth consumption. Poseidon supports complex FHE operations like Bootstrapping, Keyswitch, Rotation and so on, under limited FPGA resources. It refines these operations by abstracting five key operators: Modular Addition (MA), Modular Multiplication (MM), Number Theoretic Transformation (NTT), Automorphsim and Shared Barret Reduction (SBT). These operators are combined and reused to implement higher-level FHE operations. To utilize the FPGA resources more efficiently and improve the parallelism, we adopt the radix-based NTT algorithm and propose HFAuto, an optimized automorphism implementation suitable for FPGA. Then, we design the hardware accelerator based on the optimized key operators and HBM to maximize computational efficiency. We evaluate Poseidon with four domain-specific FHE benchmarks on Xilinx Alveo U280 FPGA. Empirical results show that the efficient reuse of the operator cores and on-chip storage enables superior performance compared with the state-of-the-art GPU, FPGA and accelerator ASICs. We highlight the following results: (1) up to 370× speedup over CPU for the basic operations of FHE; (2) up to 1300×/52× speedup over CPU and the FPGA solution for the key operators; (3) up to 10.6×/8.7× speedup over GPU and the ASIC solution for the FHE benchmark.
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引用它的顶会 Paper11
- Trinity: A General Purpose FHE AcceleratorXianglong Deng, Shengyu Fan, Zhicheng Hu, Zhuoyu Tian 等MICRO 2024 · 被引用 34 次
- Strix: An End-to-End Streaming Architecture with Two-Level Ciphertext Batching for Fully Homomorphic Encryption with Programmable BootstrappingAdiwena Putra, Prasetiyo, Yi Chen, John Kim 等MICRO 2023 · 被引用 27 次
- A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic EncryptionAleksandar Krastev, Nikola Samardzic, Simon Langowski, Srinivas Devadas 等PLDI 2024 · 被引用 23 次
- GPU-based Private Information Retrieval for On-Device Machine Learning InferenceMaximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng 等ASPLOS 2024 · 被引用 11 次
- EFFACT: A Highly Efficient Full-Stack FHE Acceleration PlatformYi Huang, Xinsheng Gong, Xiangyu Kong, Dibei Chen 等HPCA 2025 · 被引用 10 次
它引用的顶会 Paper8
- Fast Private Set Intersection from Homomorphic EncryptionHao Chen, Kim Laine, Peter RindalCCS 2017 · 被引用 446 次
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas 等MICRO 2021 · 被引用 294 次
- HEAX: An Architecture for Computing on Encrypted DataM. Sadegh Riazi, Kim Laine, Blake Pelton, Wei DaiASPLOS 2020 · 被引用 244 次
- CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUSijun Tan, Brian Knott, Yuan Tian, David J. WuS&P 2021 · 被引用 241 次
- CraterLake: a hardware accelerator for efficient unbounded computation on encrypted dataNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Nathan Manohar 等ISCA 2022 · 被引用 205 次
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