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Semantic Scholar Open Access 2021 312 sitasi

Machine Learning for Electronic Design Automation: A Survey

Guyue Huang Jingbo Hu Yifan He Jialong Liu Mingyuan Ma +11 lainnya
Lihat Sumber DOI

Abstrak

With the down-scaling of CMOS technology, the design complexity of very large-scale integrated is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 1990s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interest in incorporating ML to solve EDA tasks. In this article, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

Topik & Kata Kunci

Computer Science Engineering

Penulis (16)

G

Guyue Huang

J

Jingbo Hu

Y

Yifan He

J

Jialong Liu

M

Mingyuan Ma

Z

Zhaoyang Shen

J

Juejian Wu

Y

Yuanfan Xu

H

Hengrui Zhang

K

Kai Zhong

X

Xuefei Ning

Y

Yuzhe Ma

H

Haoyu Yang

B

Bei Yu

H

Huazhong Yang

Y

Yu Wang

Format Sitasi

APA MLA BibTeX
Huang, G., Hu, J., He, Y., Liu, J., Ma, M., Shen, Z. et al. (2021). Machine Learning for Electronic Design Automation: A Survey. https://doi.org/10.1145/3451179

Akses Cepat

Lihat di Sumber doi.org/10.1145/3451179
Informasi Jurnal
Tahun Terbit
2021
Bahasa
en
Total Sitasi
312×
Sumber Database
Semantic Scholar
DOI
10.1145/3451179
Akses
Open Access ✓