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APACHE: A Processing-Near-Memory Architecture for Multi-Scheme Fully Homomorphic Encryption
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DOI：10.1109/TC.2026.3671835.png)
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Abstract
En 中文
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. Nevertheless, 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 evaluated APACHE in various FHE applications, such as Lola MNIST, HELR, fully packed bootstrapping, and fully homomorphic processors. The results illustrate that APACHE outperforms state-of-the-art ASIC FHE accelerators by 10.63<inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> to 35.47<inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> over a variety of operator and application benchmarks.
Keywords:
Fully homomorphic encryption
privacy-preserving computation
processing-near-memory
hardware acceleration
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