APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully Homomorphic Encryption
<|reference_start|>APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully Homomorphic Encryption: Fully Homomorphic Encryption (FHE) allows one to outsource computation over encrypted data to untrusted servers without worrying about data breaching. Since 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 APACHE, 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 both computational resources and memory bandwidth. In addition, we propose a multi-scheme operator compiler to efficiently schedule high-level FHE computations across lower-level functional units. In the experiment, we evaluate APACHE on various FHE applications, such as Lola MNIST, HELR, fully-packed bootstrapping, and fully homomorphic processors. The results illustrate that APACHE outperforms the state-of-the-art ASIC FHE accelerators by 2.4x to 19.8x over a variety of operator and application benchmarks.<|reference_end|>
@article{ding2024apache:,
    title={APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully
  Homomorphic Encryption},
    author={Lin Ding, Song Bian, Penggao He, Yan Xu, Gang Qu, and Jiliang Zhang},
    journal={arXiv preprint arXiv:2404.15819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15819},
    primaryClass={cs.AR}
}