With the outbreak of novel coronavirus in 2019, most universities changed from traditional offline teaching to online teaching, which brought about a large amount of problems, including teachers’ physical and mental problems. Because of teaching on the computer screen for a long period of time, the teacher lacks communication and can act casually. With long-term accumulation, the problem of teachers’ job burnout has become increasingly serious. The main purpose of this study was to examine the influence of professional identity on job burnout during the period of the novel coronavirus. At the same time, this study also discussed the moderating effect of job satisfaction on professional identity and job burnout, and its relationship between job satisfaction and job burnout. During the peak period of the COVID-19 epidemic, we conducted an online survey—483 Chinese university teachers with online teaching experience completed the Teacher Professional Identity Scale, Job Satisfaction Scale, and Job Burnout Scale. The results of this study found professional identity and job satisfaction of university teachers to be significantly negative predictors of job burnout, with job satisfaction playing a moderating role between professional identity and job burnout. This study also confirmed that professional identity and job satisfaction are important factors affecting job burnout of university teachers. Therefore, this study proposed that schools should adopt more effective strategies to improve university teachers’ professional identity and job satisfaction in order to reduce the practical problems of job burnout, ensure the effectiveness of online teaching, and maintain the sustainable development during the epidemic.

IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vectorless IR analyses. Moreover, the proposed CNN model is general and transferable to different designs. This is in contrast to most existing machine learning (ML) approaches, where a model is applicable only to a specific design. Experimental results show that PowerNet outperforms the latest ML method by 9% in accuracy for the challenging case of vectorless IR drop and achieves a 30× speedup compared to an accurate IR drop commercial tool. Further, a mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively, with very limited modification on their power grids.

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Early routability prediction helps designers and tools perform preventive measures so that design rule violations can be avoided in a proactive manner. However, it is a huge challenge to have a predictor that is both accurate and fast. In this work, we study how to leverage convolutional neural network to address this challenge. The proposed method, called RouteNet, can either evaluate the overall routability of cell placement solutions without global routing or predict the locations of DRC (Design Rule Checking) hotspots. In both cases, large macros in mixed-size designs are taken into consideration. Experiments on benchmark circuits show that RouteNet can forecast overall routability with accuracy similar to that of global router while using substantially less runtime. For DRC hotspot prediction, RouteNet improves accuracy by 50% compared to global routing. It also significantly outperforms other machine learning approaches such as support vector machine and logistic regression.