FLASH-FHE: A Heterogeneous Architecture for Fully Homomorphic Encryption Acceleration

TL;DR

FLASH-FHE introduces a heterogeneous hardware architecture with specialized clusters to efficiently accelerate mixed shallow and deep FHE workloads, significantly outperforming previous accelerators.

Contribution

It is the first to design a heterogeneous FHE accelerator with dedicated clusters for different workload depths, optimizing performance and resource sharing.

Findings

Achieves 1.4x to 11.2x speedup over state-of-the-art accelerators for deep workloads.

Delivers up to 8.0x speedup for shallow workloads.

Demonstrates effective resource sharing and dynamic workload scheduling.

Abstract

While many hardware accelerators have recently been proposed to address the inefficiency problem of fully homomorphic encryption (FHE) schemes, none of them is able to deliver optimal performance when facing real-world FHE workloads consisting of a mixture of shallow and deep computations, due primarily to their homogeneous design principle. This paper presents FLASH-FHE, the first FHE accelerator with a heterogeneous architecture for mixed workloads. At its heart, FLASH-FHE designs two types of computation clusters, ie, bootstrappable and swift, to optimize for deep and shallow workloads respectively in terms of cryptographic parameters and hardware pipelines. We organize one bootstrappable and two swift clusters into one cluster affiliation, and present a scheduling scheme that provides sufficient acceleration for deep FHE workloads by utilizing all the affiliations, while improving parallelism for shallow FHE workloads by assigning one shallow workload per affiliation and dynamically decomposing the bootstrappable cluster into multiple swift pipelines to accelerate the assigned workload. We further show that these two types of clusters can share valuable on-chip memory, improving performance without significant resource consumption. We implement FLASH-FHE with RTL and synthesize it using both 7nm and 14/12nm technology nodes, and our experiment results demonstrate that FLASH-FHE achieves an average performance improvement of $1.4\times$ and $11.2\times$ compared to state-of-the-art FHE accelerators CraterLake and F1 for deep workloads, while delivering up to $8.0\times$ speedup for shallow workloads due to its heterogeneous architecture.