On Predicting Solution Quality of Maze Routing Using Convolutional Neural Network

Chang, Kuei-Huan; Pan, Hsin-Hung; Wang, Ting-Chi; Chen, Po‐Yuan; Shen, Chin-Fang Cindy

doi:10.1109/isqed54688.2022.9806227

Cited by 1 publication

(1 citation statement)

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“…Deep learning methods can be used to learn effective routing strategies from large amounts of routing data. For instance, convolutional neural networks (CNNs) can be used to analyze the local routing environment and predict the best direction for routing a wire [11]. Another example can be Graph neural net works(GNN), which is proved to be very efficient on routing and placing [12].…”

Section: Key Conceptsmentioning

confidence: 99%

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

Shen

2023

ACE

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Rapid advances in artificial intelligence (AI) and machine learning have had a significant impact on various fields, including electronic design automation (EDA). This study aims to compare current EDA routing algorithms with AI-based routing algorithms, highlighting their respective advantages and limitations. Through a comprehensive analysis of existing EDA routing algorithms and artificial intelligence-based technologies, this study explores the applicability of these algorithms in different EDA scenarios, with a focus on their effectiveness and efficiency in routing tasks, and aims to compare and contrast traditional and AI-based routing algorithms in the context of electronic design automation. It also shows that the current AI technology will play an important role in the future development of EDA software. Therefore, the combination of AI technology may be the focus of EDA software development, so as to help elucidate the evolving landscape of EDA and provide insights into the potential future direction, and provide a a comprehensive understanding of their underlying methodologies, advantages, and limitations of traditional and AI-based algorithms in the context of electronic design automation.

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Section: Key Conceptsmentioning

confidence: 99%

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

Shen

2023

ACE

View full text Add to dashboard Cite

show abstract

On Predicting Solution Quality of Maze Routing Using Convolutional Neural Network

Cited by 1 publication

References 11 publications

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

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