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.

本文提出了 Falcon，一种用于基于 HE 的二方计算框架的高效密实打包算法，它通过零感知贪心打包算法和通信感知操作器平铺策略，提高了深度卷积的打包密度，在操作器级别上相比于其他 HE-based 2PC 框架，实现了 15.6x、5.1x 和 1.8x 的延迟降低，并且在网络级别上，在 CIFAR-100 和 TinyImagenet 数据集上相较于 Cheetah 分别提高了 1.4% 和 4.2% 的准确率，并且具有等通信的特征。