AI RESEARCH
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
arXiv CS.LG
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ArXi:2604.11659v1 Announce Type: cross 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.