最新消息

Date

Title

Content

Important Dates

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

【Annoucement】

Online Paper Submission Link for TAAI 2023 is released.

Aug. 25, 2023

Submission

Please note that papers must submit via the submission system website and meet the format of TAAI 2023. Papers for the international track have to be written and presented in English. You must submit :

(1) Full paper in PDF format and formatted using the Springer A4 format, including the bibliography within 12-15 pages.

(2) Paper abstract in Word format.

Important Information :

After the paper is accepted, an authorization agreement needs to be signed 

At least one author per accepted paper must register and attend the conference to present for the accepted submission to be included in the conference proceedings.

Springer Manuscript Template:Download.

Please read and understand the Instructions for Authors of Published Papers Springer Journal of Computer Science first.

Paper Abstract Template:Download 

Submission Link :

Session Organizer : Prof. Dun-Wei Cheng
Instituion : National Yunlin University of Science and Technology
E-mail : dunwei@yuntech.edu.tw

The use of ML for EDA has become a trending topic, with numerous studies proposing the use of ML to improve EDA methods. These studies cover almost all stages of the chip design process, including design space reduction and exploration, logic synthesis, placement, routing, testing, verification, manufacturing, etc. The special session on Machine Learning for EDA aims to apply ML techniques to accelerate EDA tasks. ML methods show great potential in generating high-quality solutions

Session Organizer : Prof. Rung-Ching Chen and Prof. Alex Long-Sheng Chen
Instituion : Chaoyang University of Technology
E-mail : crching@cyut.edu.tw , lschen@cyut.edu.tw

Deep Learning combines high-performance computing leading to unusual solutions for multi-model data analysis problems. Deep Learning-empowered systems can nowadays achieve performance levels in various data analysis tasks comparable to, or even exceeding, those of human beings. These advancements have the potential to open new high-impact applications in different environments. In this special issue, the authors will fully exploit Deep Learning solutions in smart applications through theoretical, methodological, and experimental contributions.

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