Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU

TL;DR

Flash is a hybrid private inference protocol for deep CNNs that achieves high accuracy and low latency on CPU by combining optimized algorithms, novel training strategies, and efficient secure computation techniques.

Contribution

The paper introduces a new hybrid private inference protocol that significantly reduces latency and communication costs for deep CNNs on CPU, with novel algorithms and training methods.

Findings

End-to-end inference latency less than 1 minute on CPU for deep CNNs.

Achieves 4-94x performance gain over state-of-the-art in convolution operations.

Reduces communication cost by 84-196x for activation evaluation.

Abstract

This paper presents Flash, an optimized private inference (PI) hybrid protocol utilizing both homomorphic encryption (HE) and secure two-party computation (2PC), which can reduce the end-to-end PI latency for deep CNN models less than 1 minute with CPU. To this end, first, Flash proposes a low-latency convolution algorithm built upon a fast slot rotation operation and a novel data encoding scheme, which results in 4-94x performance gain over the state-of-the-art. Second, to minimize the communication cost introduced by the standard nonlinear activation function ReLU, Flash replaces the entire ReLUs with the polynomial $x^2+x$ and trains deep CNN models with the new training strategy. The trained models improve the inference accuracy for CIFAR-10/100 and TinyImageNet by 16% on average (up to 40% for ResNet-32) compared to prior art. Last, Flash proposes an efficient 2PC-based $x^2+x$ evaluation protocol that does not require any offline communication and that reduces the total communication cost to process the activation layer by 84-196x over the state-of-the-art. As a result, the end-to-end PI latency of Flash implemented on CPU is 0.02 minute for CIFAR-100 and 0.57 minute for TinyImageNet classification, while the total data communication is 0.07GB for CIFAR-100 and 0.22GB for TinyImageNet. Flash improves the state-of-the-art PI by 16-45x in latency and 84-196x in communication cost. Moreover, even for ImageNet, Flash can deliver the latency less than 1 minute on CPU with the total communication less than 1GB.