no code implementations • 19 Jul 2024 • Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang, Ningyuan Cao, Yiran Chen, Xin Zhang
To this end, we introduce LaMAGIC, a pioneering language model-based topology generation model that leverages supervised finetuning for automated analog circuit design.
1 code implementation • 14 Dec 2023 • Qijun Zhang, Shiyu Li, Guanglei Zhou, Jingyu Pan, Chen-Chia Chang, Yiran Chen, Zhiyao Xie
Based on the formulation, we propose PANDA, an innovative architecture-level solution that combines the advantages of analytical and ML power models.
no code implementations • 4 Dec 2023 • Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Yiran Chen
The application of Machine Learning (ML) in Electronic Design Automation (EDA) for Very Large-Scale Integration (VLSI) design has garnered significant research attention.
no code implementations • 30 Mar 2022 • Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, Yiran Chen
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
no code implementations • 20 Mar 2022 • Zhiyao Xie, Jingyu Pan, Chen-Chia Chang, Yiran Chen
The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies.
no code implementations • 3 Dec 2020 • Chen-Chia Chang, Jingyu Pan, Tunhou Zhang, Zhiyao Xie, Jiang Hu, Weiyi Qi, Chun-Wei Lin, Rongjian Liang, Joydeep Mitra, Elias Fallon, Yiran Chen
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.