National Taiwan University of Science and Technology

About: National Taiwan University of Science and Technology is a education organization based out in Taipei, Taipei, Taiwan. It is known for research contribution in the topics: Fuzzy logic & Control theory. The organization has 16288 authors who have published 21577 publications receiving 426294 citations. The organization is also known as: Taiwan Tech & Taiwantech.

Examining Practicing Teachers' Perceptions of Technological Pedagogical Content Knowledge (TPACK) Pathways: A Structural Equation Modeling Approach.

Abstract: The seven constructs of the technological pedagogical content knowledge (TPACK) framework has been widely adopted as a theoretical basis for understanding the scope of teachers’ information and communication technology (ICT) expertise. Despite this, very little is understood about the inter-relationships between these constructs, especially how these relationships are related to teachers’ TPACK. As a result, the theory–practice nexus of this framework remains weak. In this study, a structural equation model based on Mishra and Koehler’s TPACK framework was developed to describe the TPACK perceptions of 455 practicing teachers in Singapore. The study shows that teachers, perceived TPACK to be formulated from the direct effects of technological knowledge and pedagogical knowledge. They also perceived these knowledge sources to contribute to the development of technological pedagogical knowledge and technological content knowledge, which also contributed to their TPACK. In these teachers’ conceptions of TPACK, however, the effects of content knowledge and pedagogical content knowledge were not evident. The implications of these relationships to the design of teacher professional development in ICT are discussed.

RouteNet: routability prediction for mixed-size designs using convolutional neural network

Abstract: 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.