Poseidon: Practical Homomorphic Encryption Accelerator
Yinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu, Mingzhe Zhang, Xiaowei Li
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers11
- Trinity: A General Purpose FHE AcceleratorXianglong Deng, Shengyu Fan, Zhicheng Hu, Zhuoyu Tian et al.MICRO 2024 · 34 citations
- 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 et al.MICRO 2023 · 27 citations
- A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic EncryptionAleksandar Krastev, Nikola Samardzic, Simon Langowski, Srinivas Devadas et al.PLDI 2024 · 23 citations
- GPU-based Private Information Retrieval for On-Device Machine Learning InferenceMaximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng et al.ASPLOS 2024 · 11 citations
- EFFACT: A Highly Efficient Full-Stack FHE Acceleration PlatformYi Huang, Xinsheng Gong, Xiangyu Kong, Dibei Chen et al.HPCA 2025 · 10 citations
Builds on8
- Fast Private Set Intersection from Homomorphic EncryptionHao Chen, Kim Laine, Peter RindalCCS 2017 · 446 citations
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas et al.MICRO 2021 · 294 citations
- HEAX: An Architecture for Computing on Encrypted DataM. Sadegh Riazi, Kim Laine, Blake Pelton, Wei DaiASPLOS 2020 · 244 citations
- CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUSijun Tan, Brian Knott, Yuan Tian, David J. WuS&P 2021 · 241 citations
- CraterLake: a hardware accelerator for efficient unbounded computation on encrypted dataNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Nathan Manohar et al.ISCA 2022 · 205 citations
Related papers
- An Efficient and Scalable Hardware Architecture for Number Theoretic Transform on FPGA with Design AutomationYilan Zhu, Geng Yang, Xingyu Tian, Dilshan Kumarathunga et al.HPCA 2026 · 1 citation
- Ares: High Performance Near-Storage Accelerator for FHE-based Private Set IntersectionHaoxuan Wang, Yinghao Yang, Jinkai Zhang, Hang Lu et al.DAC 2025 · 1 citation
- Anaheim: Architecture and Algorithms for Processing Fully Homomorphic Encryption in MemoryJongmin Kim, Sungmin Yun, Hyesung Ji, Wonseok Choi et al.HPCA 2025 · 14 citations
- FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic EncryptionMichiel Van Beirendonck, Jan-Pieter D'Anvers, Furkan Turan, Ingrid VerbauwhedeCCS 2023 · 28 citations
- FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic EncryptionRashmi Agrawal, Leo de Castro, Guowei Yang, Chiraag Juvekar et al.HPCA 2023 · 136 citations
