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arXiv · 2604.11659

GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs

Lara D'Agata·Carlos Agulló-Domingo·Óscar Vera-López·Kaustubh Shivdikar·Ardhi W. B. Yudha·Ferhat Yaman·David Kaeli·José L. Abellán·Ian Colbert·José Cano
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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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Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López, Kaustubh Shivdikar, Ardhi W. B. Yudha, Ferhat Yaman, David Kaeli, José L. Abellán, Ian Colbert, José Cano. 2026-04-13. GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs. https://arxiv.org/abs/2604.11659

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