Reinforcement Learning-Based Optimization of Back-Side Power Delivery Networks in VLSI Design for IR -Drop Reduction-Reference-Cited by-同舟云学术

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Reinforcement Learning-Based Optimization of Back-Side Power Delivery Networks in VLSI Design for IR -Drop Reduction

Published:2024-03-25 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Woo Seungmin¹,Lee Hyunsoo¹,Shin Yunjeong¹,Han MinSeok¹,Go Yunjeong¹,Kim Jongbeom¹,Lee Hyundong¹,Kim Hyunwoo¹,Song Taigon¹

Affiliation:

1. Kyungpook National University,Daegu,South Korea,41566

Funder

IC Design Education Center(IDEC), Korea

National RD Program through the National Research Foundation of Korea(NRF)

Ministry of Education, Korea

National Research Foundation of Korea(NRF)

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10546498/10546499/10546599.pdf?arnumber=10546599

Reference11 articles.

1. Fast IR drop estimation with machine learning;Xie

2. Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design

3. Opportunities of Chip Power Integrity and Performance Improvement through Wafer Backside (BS) Connection

4. NS3K: A 3-nm Nanosheet FET Standard Cell Library Development and its Impact

5. Buried Power Rails and Back-side Power Grids: Arm® CPU Power Delivery Network Design Beyond 5nm

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Extensive Review of Recent Technologies and Trends in Low-Power VLSI Design;International Journal of Advanced Research in Science, Communication and Technology;2024-09-03

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