# Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2408.09593 · HTML: https://mlsanity.com/paper/2408.09593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design](https://mlsanity.com/paper/2609.16898/) (2026)

## Abstract (arXiv)

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.