Paper ID: 2308.13189

Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference

Tianshi Xu, Meng Li, Runsheng Wang, Ru Huang

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 packing algorithm and a communication-aware operator tiling strategy to improve the packing density for depthwise convolutions. Compared to SOTA HE-based 2PC frameworks, e.g., CrypTFlow2, Iron and Cheetah, Falcon achieves more than 15.6x, 5.1x and 1.8x latency reduction, respectively, at operator level. Meanwhile, at network level, Falcon allows for 1.4% and 4.2% accuracy improvement over Cheetah on CIFAR-100 and TinyImagenet datasets with iso-communication, respecitvely.

Submitted: Aug 25, 2023