Lune

ISCA2026顶会

AutoFHE: An Automatic Hardware Generation Framework for Domain-Specific FHE Accelerators

Yibo Du, Cangyuan Li, Bing Li, Mengdi Wang, Lian Liu, Shixin Zhao, Yinhe Han, Ying Wang

2026年份

摘要

Fully Homomorphic Encryption (FHE) is a transformative technology that enables computation directly on encrypted data, unlocking secure applications such as privacy inference, encrypted databases, and privacy-preserving analytics. As the demand for high-performance FHE computation grows, domainspecific FHE accelerators have emerged to improve efficiency across different application domains. However, designing these accelerators remains prohibitively difficult. It requires implementing specialized ciphertext processing elements (CPEs) and navigating a vast design space tightly coupled to cryptographic operations, demanding rare dual-domain expertise in both hardware architecture and FHE algorithms. In this paper, we propose AutoFHE, the first automatic hardware generation framework that transforms conventional domain-specific RTL accelerator designs into encrypted accelerators that operate on FHE ciphertexts. AutoFHE decouples FHE complexity from architecture design, empowering hardware designers without FHE expertise to implement FHE accelerators with significantly reduced design effort. To achieve this, (1) AutoFHE raises the abstraction level of encrypted signals, allowing designers to declaratively specify encrypted signals in a hardware construction language (Chisel) without manually modifying the underlying hardware. (2) AutoFHE operates on FIRRTL (Flexible Intermediate Representation for Register Transfer Level) to automatically identify encrypted PEs and synthesize them using optimized CPE templates. (3) To address the prohibitive resource cost, AutoFHE introduces a CPE-virtualization strategy that virtualizes a pool of physical CPEs for the identified encrypted PEs. AutoFHE develops a heuristic algorithm to search for the optimal schedule from logically identified encrypted PEs to physical CPEs, and embeds this algorithm into its design space exploration. Evaluations on multiple TFHE design cases show that AutoFHE-generated FHE accelerators outperform handcrafted designs while drastically reducing design effort.

问问这篇 Paper

问问你的智能体。

Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。

可以从这些问题问起

智能体调用

Lunesearch_papers

在 Lune 里问

免费开始,无需绑卡

lune papers get d4464fb1-98ce-43f1-b930-d63506a97f1a

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖