UNIT: A Highly Unified and Memory-Efficient FPGA-Based Accelerator for Torus FHE

Fully Homomorphic Encryption (FHE) has emerged as a promising solution for the secure computation on encrypted data without leaking user privacy. Among various FHE schemes, Torus FHE (TFHE) distinguishes itself by its ability to perform exact computations on non-linear functions within the encrypted domain, satisfying the crucial requirement for privacy-preserving AI applications. However, the high computational overhead and strong data dependency in TFHE's bootstrapping process present significant challenges to its practical adoption and efficient hardware implementation. Existing TFHE accelerators on various hardware platforms still face limitations in terms of performance, flexibility, and area efficiency. In this work, we propose UNIT, a novel and highly unified accelerator for Programmable Bootstrapping (PBS) in TFHE, featuring carefully designed computation units. We introduce a unified architecture for negacyclic (inverse) number theoretic transform (I)NTT with fused twisting steps, which reduces computing resources by 33% and the memory utilization of pre-stored factors by nearly 66 %. Another key feature of UNIT is the innovative design of the monomial number theoretic transform unit, called OF-MNTT, which leverages on-the-fly twiddle factor generation to eliminate memory traffic and overhead. This memory-efficient and highly parallelizable approach for MNTT is proposed for the first time in TFHE acceleration. Furthermore, UNIT is highly reconfigurable and scalable, supporting various parameter sets and performance-resource requirements. Our proposed accelerator is evaluated on the Xilinx Alveo U250 FPGA platform. Experimental results demonstrate its superior performance compared to the state-of-the-art GPU and FPGA-based implementations with the improvement of 8.3x and 3.63x, respectively. In comparison with the most advanced FPGA implementation, UNIT achieves 30% enhanced area efficiency and 3.2 x reduced power with much better flexibility.