A Framework for Generating Accelerators for Homomorphic Encryption Operations on FPGAs

Yang Yang, Rajgopal Kannan, Viktor K. Prasanna

Abstract: Homomorphic Encryption (HE) is a promising technique for preserving user data privacy in cloud computing. Nevertheless, HE operations are magnitudes slower than un-encrypted computations due to their high computational complexity. FPGAs are attractive platforms for designing domain-specific accelerators. However, manually programming FPGAs for HE applications is nontrivial because of the vastly different parameter settings and latency requirements. To close the gap, we propose a framework to generate low latency FPGA accelerators for all the operations supported by HE, enabling users to utilize FPGA-accelerated HE processing without requiring knowledge of FPGA implementation details. The framework takes HE parameters and hardware resource constraints as input, uses design space exploration to automatically determine the design parameters that minimize HE computation latency, and produce synthesizable Verilog code. We propose a layered approach that decomposes HE operations into basic HE primitives, coupled with a parameterized HE domain-specific architecture that can efficiently execute the HE primitives. This approach avoids allocating dedicated FPGA resources to different subroutines within HE operations and improves compute utilization. Our evaluation shows that the generated accelerators significantly reduce latency in various HE operations, achieving up to $215\times$ improvement over state-of-the-art CPU implementations. We demonstrate our framework's capability to compose end-to-end HE applications using HE CNN inference. Our designs outperform state-of-the-art CPU designs in latency by up to $60\times$ .