THENA: Accelerating Torus Fully Homomorphic Encryption on Energy-Efficient Heterogeneous Architecture

Abstract

Fully Homomorphic Encryption (FHE) enables privacy-preserving computations on encrypted data with strong security guarantees. Torus-based FHE (TFHE) emerges as a promising candidate among FHE variants due to its efficient Boolean logic operation and unlimited computational depth. However, it heavily relies on bootstrapping, a computationally intensive technique. Although there has been significant progress in improving the throughput and latency of the bootstrapping process, there exists a gap in the energy efficiency research of this process without compromising its speed. Also, energy-efficient implementation of TFHE is a key requirement for its application in energy-constrained systems. This work introduces THENA, an energy-efficient bootstrapping accelerator for TFHE built on a heterogeneous Versal adaptive system on chip (ASoC) platform to address this gap. THENA partitions the bootstrapping workload into different parts of ASoC: the serial operations are handled by the processing system (PS), the compute-intensive torus multiplications are mapped to the adaptive intelligent engine (AIE), and the memory and communication operations are allocated on the programming logic (PL). THENA derives a wavefront array-based energy-efficient multiplier, achieving a higher improvement in throughput over a similar implementation (SaberNTT, TCAS '23). THENA uses this multiplier to deliver an end-to-end bootstrapping accelerator on the Versal VCK-190 platform. THENA delivers better energy efficiency for bootstrapping than GPU-based CuFHE (RTX 3090) and outperforms existing complete FPGA designs, such as YKP (HPEC '22) by demonstrating 17.5%, and 35.6% decrease in latency and energy consumption. To the best of our knowledge, this is the first PS+PL+AIE-based heterogeneous TFHE accelerator on Versal ASoCs. THENA's code and experimental artifacts are published on GitHub.

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