Homomorphic Encryption (HE) enables secure computation on encrypted data, addressing privacy concerns in cloud computing. However, the high computational cost of HE operations, particularly matrix multiplication (MM), remains a major barrier to its practical deployment. Accelerating homomorphic encrypted MM (HE MM) is therefore crucial for applications such as privacy-preserving machine learning. In this paper, we present a bandwidth-efficient FPGA implementation of HE MM. We first develop a cost model to evaluate the on-chip memory requirements for a given set of HE parameters and input matrix sizes. Our analysis shows that optimizing on-chip memory usage is critical for scalable and efficient HE MM. To this end, we design a novel datapath for Homomorphic Linear Transformation (HLT), the primary bottleneck in HE MM. The proposed datapath significantly reduces off-chip memory traffic and on-chip memory demand by enabling fine-grained data reuse. Leveraging this datapath, we introduce FAME, the first FPGA-based accelerator specifically tailored for HE MM. FAME supports arbitrary matrix shapes and is configurable across a wide range of HE parameter sets. We implement FAME on an Alveo U280 FPGA and evaluate its performance across diverse matrix sizes and shapes. Experimental results show that FAME achieves an average speedup of 221x over state-of-the-art CPU-based implementations, demonstrating its scalability and practicality for large-scale consecutive HE MM and real-world workloads.

翻译：同态加密（HE）支持对加密数据进行安全计算，有效应对云计算中的隐私问题。然而，HE运算的高计算成本，尤其是矩阵乘法（MM），仍是其实际部署的主要障碍。因此，加速同态加密矩阵乘法（HE MM）对于隐私保护机器学习等应用至关重要。本文提出了一种带宽高效的HE MM FPGA实现方案。我们首先构建了一个成本模型，用于评估给定HE参数集和输入矩阵尺寸下的片上内存需求。分析表明，优化片上内存使用对于实现可扩展且高效的HE MM至关重要。为此，我们为HE MM中的主要瓶颈——同态线性变换（HLT）设计了一种新颖的数据通路。该数据通路通过实现细粒度数据复用，显著降低了片外内存流量和片上内存需求。基于此数据通路，我们提出了FAME，这是首个专为HE MM定制的基于FPGA的加速器。FAME支持任意矩阵形状，并可在广泛的HE参数集范围内进行配置。我们在Alveo U280 FPGA上实现了FAME，并在不同矩阵尺寸和形状下评估其性能。实验结果表明，FAME相比最先进的基于CPU的实现方案平均加速比达到221倍，证明了其在大规模连续HE MM和实际工作负载中的可扩展性和实用性。