부산대학교 도서관

Quantization inference has been ubiquitously utilized in edge computing devices, enabling numerous neural networks' efficient deployment. However, the existing methods generally concentrate on classical network structures like VGG-16 and ResNet, ignoring the emerging compact networks such as MobileNets. Specifically, these networks with lightweight architectures have fewer parameters; thereby, less redundancy can be exploited in the quantization stage, which often incurs intolerable accuracy degradations. To handle these problems, we propose a novel Rounding Shift Channel (RSC) post-training quantization method using layer search. We alleviate the accuracy degradation via channel-wise scaling by minimizing a layer-wise reconstruction quantization error. Furthermore, we design a rounding-shift operation to correct biased inference, which has not been explored in previous integer-only-inference studies. The experiments carried on ILSVRC-2012 show that we can achieve 70.1%and 69.9% top-l accuracy for MobileNet V1 and V2, respectively, manifesting the effectiveness of our method. We open-sourced our code at https://github.com/xumengmeng-97IMV-Quantization.