An Architecture to Accelerate Computation on Encrypted Data

IEEE Micro(2022)

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摘要
Fully homomorphic encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is prohibitively expensive when executed in software. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. F1 is the first system to accelerate complete FHE programs, and outperforms state-of-the-art software implementations by gmean 5,400x. These speedups counter FHE's overheads and enable new applications, like real-time private deep learning in the cloud.
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关键词
real-time private deep learning,number-theoretic transforms,FHE widespread adoption,FHE overheads,accelerate computation,software implementations,complete FHE programs,data movement,modular arithmetic number-theoretic transforms,FHE primitives,wide-vector processor,acceleration opportunities,FHE computations,in-depth architectural analysis,FHE accelerator,ideal security,untrusted servers,encrypted data,fully homomorphic encryption
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