# SoK: Fully Homomorphic Encryption Accelerators

arXiv: https://arxiv.org/abs/2212.01713 · HTML: https://mlsanity.com/paper/2212.01713/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Leveraging ASIC AI Chips for Homomorphic Encryption](https://mlsanity.com/paper/2501.07047/) (2025)
- [A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness](https://mlsanity.com/paper/2509.22721/) (2025)

## Abstract (arXiv)

Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and extremely time-consuming ciphertext maintenance operations. To tackle this challenge, various FHE accelerators have recently been proposed by both research and industrial communities. This paper takes the first initiative to conduct a systematic study on the 14 FHE accelerators -- cuHE/cuFHE, nuFHE, HEAT, HEAX, HEXL, HEXL-FPGA, 100×, F1, CraterLake, BTS, ARK, Poseidon, FAB and TensorFHE. We first make our observations on the evolution trajectory of these existing FHE accelerators to establish a qualitative connection between them. Then, we perform testbed evaluations of representative open-source FHE accelerators to provide a quantitative comparison on them. Finally, with the insights learned from both qualitative and quantitative studies, we discuss potential directions to inform the future design and implementation for FHE accelerators.