一句话：提出面向全同态加密加速器的算法-硬件协同设计，以极低面积开销实现最高1.48倍加速。
全同态加密（FHE）支持在密文上直接计算，是安全云计算的重要方向，但其巨大计算开销阻碍实际部署，因此需要专用硬件加速器。然而，在FHE加速器上部署密码算法优化时，片上内存容量等硬件约束常使理论算法优势难以转化为实际硬件效率。本文提出Falcon，一种面向高效FHE加速的算法-硬件协同设计。作者首先分析主流最小密钥切换方法的计算特征，提出面向硬件的算法优化以显著降低计算开销；随后针对多簇架构优化簇间通信，同时保留计算优势；通过刻画核心原语的算术模式，设计面向硬件的算术融合与计算单元功能复用；最后提出内存自适应策略，使Falcon可在片上内存预算受限的既有加速器上有效部署。实验表明，该协同设计带来最高1.48倍加速，面积开销仅增加0.8%。
Fully homomorphic encryption (FHE) enables computation on encrypted data without compromising privacy, positioning it as a promising solution for secure cloud computing. However, its substantial computational overhead impedes practical deployment, prompting the development of dedicated hardware accelerators. In practice, when deploying cryptographic algorithm optimizations on FHE accelerators, hardware constraints typically such as limited memory capacity, often lead to a disparity between theoretical algorithmic advantage and achievable hardware efficiency. We present Falcon, an algorithm-hardware co-design for efficient FHE acceleration. We first conduct computational analysis of the prevalent minimum key-switching method, and propose hardware-oriented algorithmic optimizations that substantially reduce computational overhead. We then introduce further refinements to decrease the inherent inter-cluster communication overhead in multi-cluster architectures, while preserving computational advantages. By characterizing the arithmetic patterns intrinsic to primary primitives, we devise hardware-specific arithmetic fusion and functional reuse across computational units. Finally, we present a memory-adaptive strategy to effectively deploy Falcon to prior accelerators under constrained on-chip memory budgets. Experimental results demonstrate that applying our algorithm–hardware co-design delivers up to a 1.48× speedup with a negligible area overhead (only a 0.8% increase).
@inproceedings{0005DFFCZ0CY026,
title = {Falcon: Algorithm-Hardware Co-Design for Efficient Fully Homomorphic Encryption Accelerator},
author = {Liang Kong and Xianglong Deng and Guang Fan and Shengyu Fan and Lei Chen and Yilan Zhu and Geng Yang and Yisong Chang and Shoumeng Yan and Mingzhe Zhang},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790160}
}