智能制造技术与系统研究中心知识库

Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on edge devices. However, most of the existing methods usually need time-consuming training or fine-tuning and access to the original training dataset that may be unavailable due to privacy or security concerns. In this paper, we introduce a novel method named EvoQ that employs evolutionary search to achieve mixed precision quantization with limited data, which can optimize the resource allocation without adding computation consumption. Considering the shortage of samples and expensive search costs, we use 50 samples to measure the output difference between the quantization model and the pre-trained model for the evaluation of quantization policy, which can save the time obviously while maintaining high accuracy. To improve the search efficiency, we analyze the quantization sensitivity of each layer and utilize the results to optimize the mutation operation. At last, we calibrate the outputs and intermediate features of the quantization model using the selected 50 samples to improve the performance further. We implement extensive experiments on a diverse set of models, including ResNet18/50/101, SqueezeNet, ShuffleNetV2, and MobileNetV2 on ImageNet, as well as SSD-VGG and SSDResNet50 on PASCAL VOC. Our method can improve the performance apparently and outperforms the existing post-training quantization methods, demonstrating the effectiveness of EvoQ.