ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
Jongmin Kim, Gwangho Lee, Sangpyo Kim, Gina Sohn, Minsoo Rhu, John Kim, Jung Ho Ahn
Abstract
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we perform operations on HE-encrypted data, restricting the number of possible operations. Fully HE (FHE) removes this restriction by introducing the bootstrapping operation, which refreshes the data; however, FHE schemes are highly memory-bound. Bootstrapping, in particular, requires loading GBs of evaluation keys and plaintexts from offchip memory, which makes FHE acceleration fundamentally bottlenecked by the off-chip memory bandwidth.In this paper, we propose ARK, an Accelerator for FHE with Runtime data generation and inter-operation Key reuse. ARK enables practical FHE workloads with a novel algorithm-architecture co-design to accelerate bootstrapping. We first eliminate the off-chip memory bandwidth bottleneck through runtime data generation and inter-operation key reuse. This approach enables ARK to fully exploit on-chip memory by substantially reducing the size of the working set. On top of such algorithmic enhancements, we build ARK microarchitecture that minimizes on-chip data movement through an efficient, alternating data distribution policy based on the data access patterns and a streamlined dataflow organization of the tailored functional units – including base conversion, number-theoretic transform, and automorphism units. Overall, our codesign effectively handles the heavy computation and data movement overheads of FHE, drastically reducing the cost of HE operations, including bootstrapping.
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.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 6a7530b7-6133-419e-9ea0-6a366b50834cCited by top-tier papers29
- BOLT: Privacy-Preserving, Accurate and Efficient Inference for TransformersQi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng et al.S&P 2024 · 149 citations
- Poseidon: Practical Homomorphic Encryption AcceleratorYinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu et al.HPCA 2023 · 109 citations
- TensorFHE: Achieving Practical Computation on Encrypted Data Using GPGPUShengyu Fan, Zhiwei Wang, Weizhi Xu, Rui Hou et al.HPCA 2023 · 90 citations
- GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic EncryptionKaustubh Shivdikar, Yuhui Bao, Rashmi Agrawal, Michael Tian Shen et al.MICRO 2023 · 46 citations
- Trinity: A General Purpose FHE AcceleratorXianglong Deng, Shengyu Fan, Zhicheng Hu, Zhuoyu Tian et al.MICRO 2024 · 34 citations
Builds on12
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 1,075 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
- CraterLake: a hardware accelerator for efficient unbounded computation on encrypted dataNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Nathan Manohar et al.ISCA 2022 · 205 citations
- BTS: an accelerator for bootstrappable fully homomorphic encryptionSangpyo Kim, Jongmin Kim, Michael Jaemin Kim, Wonkyung Jung et al.ISCA 2022 · 184 citations
Related papers
- MAD: Memory-Aware Design Techniques for Accelerating Fully Homomorphic EncryptionRashmi Agrawal, Leo de Castro, Chiraag Juvekar, Anantha P. Chandrakasan et al.MICRO 2023 · 32 citations
- HEAP: A Fully Homomorphic Encryption Accelerator with Parallelized BootstrappingRashmi S. Agrawal, Anantha P. Chandrakasan, Ajay JoshiISCA 2024 · 39 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
- EFFACT: A Highly Efficient Full-Stack FHE Acceleration PlatformYi Huang, Xinsheng Gong, Xiangyu Kong, Dibei Chen et al.HPCA 2025 · 10 citations
- FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic EncryptionMichiel Van Beirendonck, Jan-Pieter D'Anvers, Furkan Turan, Ingrid VerbauwhedeCCS 2023 · 28 citations
