Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption

Fuente: arXiv
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Autores principales: Ebel, Austin, Reagen, Brandon
Formato: Preprint
Publicado: 2024
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author Ebel, Austin
Reagen, Brandon
author_facet Ebel, Austin
Reagen, Brandon
contents In this paper we show how fully homomorphic encryption (FHE) can be accelerated using a systolic architecture. We begin by analyzing FHE algorithms and then develop systolic or systolic-esque units for each major kernel. Connecting units is challenging due to the different data access and computational patterns of the kernels. We overcome this by proposing a new data tiling technique that we name limb interleaving. Limb interleaving creates a common data input/output pattern across all kernels that allows the entire architecture, named Osiris, to operate in lockstep. Osiris is capable of processing key-switches, bootstrapping, and full neural network inferences with high utilization across a range of FHE parameters. To achieve high performance, we propose a new giant-step centric (GSC) dataflow that efficiently maps state-of-the-art FHE matrix-vector product algorithms onto Osiris by optimizing for reuse and parallelism. Our evaluation of Osiris shows it outperforms the prior state-of-the-art accelerator on all standard benchmarks.
format Preprint
id arxiv_https___arxiv_org_abs_2408_09593
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption
Ebel, Austin
Reagen, Brandon
Cryptography and Security
In this paper we show how fully homomorphic encryption (FHE) can be accelerated using a systolic architecture. We begin by analyzing FHE algorithms and then develop systolic or systolic-esque units for each major kernel. Connecting units is challenging due to the different data access and computational patterns of the kernels. We overcome this by proposing a new data tiling technique that we name limb interleaving. Limb interleaving creates a common data input/output pattern across all kernels that allows the entire architecture, named Osiris, to operate in lockstep. Osiris is capable of processing key-switches, bootstrapping, and full neural network inferences with high utilization across a range of FHE parameters. To achieve high performance, we propose a new giant-step centric (GSC) dataflow that efficiently maps state-of-the-art FHE matrix-vector product algorithms onto Osiris by optimizing for reuse and parallelism. Our evaluation of Osiris shows it outperforms the prior state-of-the-art accelerator on all standard benchmarks.
title Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption
topic Cryptography and Security
url https://arxiv.org/abs/2408.09593