Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference. (arXiv:2308.13189v1 [cs.CR])

Efficient networks, e.g., MobileNetV2, EfficientNet, etc, achieves
state-of-the-art (SOTA) accuracy with lightweight computation. However,
existing homomorphic encryption (HE)-based two-party computation (2PC)
frameworks are not optimized for these networks and suffer from a high
inference overhead. We observe the inefficiency mainly comes from the packing
algorithm, which ignores the computation characteristics and the communication
bottleneck of homomorphically encrypted depthwise convolutions. Therefore, in
this paper, we propose Falcon, an effective dense packing algorithm for
HE-based 2PC frameworks. Falcon features a zero-aware greedy …

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