Robustness-aware 2-bit quantization with real-time performance for neural network

TL;DR

This paper introduces a robustness-aware 2-bit neural network quantization method that enhances accuracy and robustness using structural and spectral norm-based loss functions, enabling real-time performance on datasets like CIFAR-10 and ImageNet.

Contribution

It proposes a novel 2-bit quantization scheme based on binary neural networks and GANs, incorporating structural and robustness-aware loss functions for improved performance and adversarial robustness.

Findings

Outperforms state-of-the-art 2-bit quantization methods on CIFAR-10 and ImageNet.

Achieves robustness against FGSM adversarial attacks.

Enables real-time neural network inference with reduced precision.

Abstract

Quantized neural network (NN) with a reduced bit precision is an effective solution to reduces the computational and memory resource requirements and plays a vital role in machine learning. However, it is still challenging to avoid the significant accuracy degradation due to its numerical approximation and lower redundancy. In this paper, a novel robustness-aware 2-bit quantization scheme is proposed for NN base on binary NN and generative adversarial network(GAN), witch improves the performance by enriching the information of binary NN, efficiently extract the structural information and considering the robustness of the quantized NN. Specifically, using shift addition operation to replace the multiply-accumulate in the quantization process witch can effectively speed the NN. Meanwhile, a structural loss between the original NN and quantized NN is proposed to such that the structural information of data is preserved after quantization. The structural information learned from NN not only plays an important role in improving the performance but also allows for further fine tuning of the quantization network by applying the Lipschitz constraint to the structural loss. In addition, we also for the first time take the robustness of the quantized NN into consideration and propose a non-sensitive perturbation loss function by introducing an extraneous term of spectral norm. The experiments are conducted on CIFAR-10 and ImageNet datasets with popular NN( such as MoblieNetV2, SqueezeNet, ResNet20, etc). The experimental results show that the proposed algorithm is more competitive under 2-bit-precision than the state-of-the-art quantization methods. Meanwhile, the experimental results also demonstrate that the proposed method is robust under the FGSM adversarial samples attack.