GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López, Kaustubh Shivdikar, Ardhi W. B. Yudha + 5 more
TLDR
This paper accelerates sparse fully homomorphic encrypted deep neural networks on AMD GPUs, improving ciphertext matrix multiplication runtime and complexity.
Key contributions
- Targets FHE matrix multiplication, a computationally intensive DNN operation.
- Proposes an optimized method for ciphertext matmul using FIDESlib on AMD GPUs.
- Exploits sparsity in operands to improve performance and reduce time complexity.
- Achieves up to 3.0x speedup over CPU and reduces complexity from cubic to semi-linear.
Why it matters
Fully homomorphic encryption is vital for secure computation, particularly in machine learning. Accelerating FHE operations on GPUs significantly advances privacy-preserving AI, making secure DNNs more practical. This work demonstrates a path towards efficient, secure deep learning.
Original 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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