DAHE: Parameter-Adaptive and Memory Efficient FPGA Acceleration of Homomorphic Encryption

Abstract

While homomorphic encryption (HE) has been well-recognized as a promising data privacy protection technique, there are many challenges to the real-world deployment of HE applications. In this work, we propose a design flow for parameter-adaptive and memory-efficient FPGA acceleration of homomorphic encryption. In the framework, we explore the correlations between HE parameter selection to meet various design objectives and the huge design space due to underlying FPGA hardware resource allocation. Particularly, we demonstrate that adaptive management of the FPGA memory hierarchy is crucial to supporting diverse cryptosystem parameter selection for application-level security, accuracy, and performance requirements. We propose a resource-efficient and flexible micro-architectural design for HE operations, where data access patterns in various pipeline execution stages are optimized for high memory bandwidth utilization. Furthermore, a memory-aware performance model is built for automatic design space exploration for cryptosystem parameter selection and hardware resource provisioning. Experimental results show 1.50X and 1.16X speedup for the NTT and Rotation operations w.r.t. the state-of-the-art FPGA implementation. Meanwhile, the proposed framework generates flexible and high-performance accelerator code for real HE application kernels with different cryptosystem parameters on a wide range of FPGA devices.