Fully Homomorphic Encryption (FHE) is known to be extremely computationally-intensive, application-specific accelerators emerged as a powerful solution to narrow the performance gap. Nonetheless, due to the increasing complexities in FHE schemes per se and multi-scheme FHE algorithm designs in end-to-end privacy-preserving tasks, existing FHE accelerators often face the challenges of low hardware utilization rates and insufficient memory bandwidth. In this work, we present \NAME, a layered near-memory computing hierarchy tailored for multi-scheme FHE acceleration. By closely inspecting the data flow across different FHE schemes, we propose a layered near-memory computing architecture with fine-grained functional unit design to significantly enhance the utilization rates of computational resources and memory bandwidth. The experimental results illustrate that APACHE outperforms state-of-the-art ASIC FHE accelerators by 10.63x to 35.47x over a variety of application benchmarks, e.g., Lola MNIST, HELR, VSP, and HE3DB.
@article{arxiv.2404.15819,
title = {APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully Homomorphic Encryption},
author = {Lin Ding and Song Bian and Penggao He and Yan Xu and Gang Qu and Jiliang Zhang},
journal= {arXiv preprint arXiv:2404.15819},
year = {2024}
}