GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913026183004160 |
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| author | D'Agata, Lara Agulló-Domingo, Carlos Vera-López, Óscar Shivdikar, Kaustubh Yudha, Ardhi W. B. Yaman, Ferhat Kaeli, David Abellán, José L. Colbert, Ian Cano, José |
| author_facet | D'Agata, Lara Agulló-Domingo, Carlos Vera-López, Óscar Shivdikar, Kaustubh Yudha, Ardhi W. B. Yaman, Ferhat Kaeli, David Abellán, José L. Colbert, Ian Cano, José |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11659 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs D'Agata, Lara Agulló-Domingo, Carlos Vera-López, Óscar Shivdikar, Kaustubh Yudha, Ardhi W. B. Yaman, Ferhat Kaeli, David Abellán, José L. Colbert, Ian Cano, José Cryptography and Security Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Machine Learning Performance 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. |
| title | GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Machine Learning Performance |
| url | https://arxiv.org/abs/2604.11659 |