MA-Opt: Reinforcement Learning-Based Analog Circuit Optimization Using Multi-Actors-Reference-Cited by-同舟云学术

MA-Opt: Reinforcement Learning-Based Analog Circuit Optimization Using Multi-Actors

Published:2024-05 Issue:5 Volume:71 Page:2045-2056
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Choi Youngchang¹^ORCID,Park Sejin¹,Choi Minjeong¹,Lee Kyongsu¹,Kang Seokhyeong¹^ORCID

Affiliation:

1. Electrical Engineering Department, Pohang University of Science and Technology, Pohang, South Korea

Funder

DRAM PIM Design Base Technology Development

Development of Memory Module and Memory Compiler for Non-Volatile PIM through the Institute of Information and Communications Technology Planning and Evaluation

Korea Government

Samsung

IC Design Education Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8919/10510354/10443951.pdf?arnumber=10443951

Reference33 articles.

1. IPRAIL—intellectual property reuse-based analog IC layout automation

2. Enabling efficient analog synthesis by coupling sparse regression and polynomial optimization;Wang

3. From Specification to Silicon: Towards Analog/Mixed-Signal Design Automation using Surrogate NN Models with Transfer Learning

4. An Artificial Neural Network Assisted Optimization System for Analog Design Space Exploration

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