Hypnos: Memory Efficient Homomorphic Processing Unit
Fully Homomorphic Encryption (FHE) introduces a novel paradigm in privacy-preserving computation, extending its applicability to various scenarios. However, Operating on encrypted data imposes significant computational challenges, particularly elevating data transmission and memory access demands. Consequently, developing an efficient system storage architecture becomes vital for FHE-specific architectures. Traditional FHE accelerators use a Host+ACC topology, often focusing on enhancing computational performance and efficient using of on-chip caches, with the assumption that very large volumes of encrypted data are already present in the accelerator’s memory while neglecting the inefficiencies of the unavoidable PCIe bus. In this paper, we propose Hypnos—a memory-efficient homomorphic encryption processing unit. In Hypnos, we abstract operators from FHE schemes into commands suitable for memory-efficient processing units and combine them with a homomorphic encryption paged memory management system designed for memory access, significantly reducing the memory access and execution time of homomorphic encryption applications. We implement Hypnos on the QianKun FPGA Card and highlight the following results: (1) outperforms SOTA ASIC and FPGA solutions by 2.58× and 4.43× (2) the communication overhead is reduced by 3.78 × compared to traditional architectures; (3) up to 27.6× and 19.06× energy efficiency improvement compared to ASIC-based CraterLake and FPGA-based Poseidon for ResNet-20 respectively.
Authors
- Hang Lu (ORCID: https://orcid.org/0000-0002-6881-660X)
- Xiaowei Li (ORCID: https://orcid.org/0000-0002-0874-814X)
- Haoxuan Wang (ORCID: https://orcid.org/0009-0003-8318-4529)
- Yinghao Yang (ORCID: https://orcid.org/0000-0002-0551-5703)
Institutions
- Chinese Academy of Sciences (CN)
- Institute of Computing Technology (CN)
Publication Details
- Published
- 2025-06-22
- DOI
- https://doi.org/10.1109/dac63849.2025.11132418
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00