Publication: GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Authors
D'Agata, Lara ; Agulló-Domingo, Carlos ; Vera-López, Óscar ; Shivdikar, Kaustubh ; Yudha, Ardhi W. B. ; Yaman, Ferhat ; Kaeli, David ; Abellán Miguel, José Luis ; Colbert, Ian ; Cano, José
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Facultades de la UMU::Facultad de Informática
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DOI
https://doi.org/10.48550/arXiv.2604.11659
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info:eu-repo/semantics/article
Description
Abstract
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated computing, FHE presents a promising opportunity for progress, with applications ranging from machine learning to information security. We target the most computationally intensive operation in deep neural networks from a hardware perspective, matrix multiplication (matmul), and adapt it for execution on AMD GPUs. We propose a new optimized method that improves the runtime and complexity of ciphertext matmul by using FIDESlib, a recent open-source FHE library designed specifically for GPUs. By exploiting sparsity in both operands, our sparse matmul implementation outperforms its CPU counterpart by up to 3.0\times and reduces the time complexity from cubic to semi-linear, demonstrating an improvement over existing FHE matmul implementations.
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Citation
arXiv:2604.11659v1
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