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F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)

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arxiv 2109.05371 v2 pith:BYVQOBQ4 submitted 2021-09-11 cs.CR cs.AR

classification cs.CRcs.AR
keywords datamovementprovidesacceleratorcomputingencryptionfirstfully
verification ladder T0 review T1 audit T2 compute T3 formal
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Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted serves. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is programmable, i.e., 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. The F1 hardware provides an explicitly managed memory hierarchy and mechanisms to decouple data movement from execution. A novel compiler leverages these mechanisms to maximize reuse and schedule off-chip and on-chip data movement. We evaluate F1 using cycle-accurate simulations and RTL synthesis. F1 is the first system to accelerate complete FHE programs and outperforms state-of-the-art software implementations by gmean 5400x and by up to 17000x. These speedups counter most of FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

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Cited by 1 Pith paper

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  1. HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption

    cs.CR 2025-06 conditional novelty 6.0 of 10

    Client-side digit decomposition and block-diagonal packing enable server-side private embedding lookups in FHE, cutting embedding-lookup latency by up to 56x versus CodedHeLUT and enabling end-to-end encrypted DLRM in...

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