Mixed precision quantization has become an important technique for enabling the execution of deep neural networks (DNNs) on limited resource computing platforms. Traditional quantization methods have primarily concentrated on maintaining neural network accuracy, either ignoring the impact of quantization on the robustness of the network, or using only empirical techniques for improving robustness. In contrast, techniques for robustness certification, which can provide strong guarantees about the robustness of DNNs have not been used during quantization due to their high computation cost. This paper introduces ARQ, an innovative mixed-precision quantization method that not only preserves the clean accuracy of the smoothed classifiers but also maintains their certified robustness. ARQ uses reinforcement learning to find accurate and robust DNN quantization, while efficiently leveraging randomized smoothing, a popular class of statistical DNN verification algorithms, to guide the search process. We compare ARQ with multiple state-of-the-art quantization techniques on several DNN architectures commonly used in quantization studies: ResNet-20 on CIFAR-10, ResNet-50 on ImageNet, and MobileNetV2 on ImageNet. We demonstrate that ARQ consistently performs better than these baselines across all the benchmarks and the input perturbation levels. In many cases, the performance of ARQ quantized networks can reach that of the original DNN with floating-point weights, but with only 1.5% instructions.

本研究解决了深度神经网络（DNN）在资源有限的计算平台上执行时的量化鲁棒性不足的问题。提出的ARQ方法通过强化学习寻找到既准确又鲁棒的DNN量化方案，同时利用随机平滑技术指导搜索过程，保证了分类器的清洁准确性和认证鲁棒性。实验结果表明，ARQ在多个基准测试中优于传统量化技术，甚至在许多情况下与原始浮点权重的DNN性能相当，但仅需1.5%的指令集。

ARQ：一种适用于准确且可证明鲁棒性的混合精度量化框架