- 专利标题: Method and system for predicting performance in electronic design based on machine learning
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申请号: US17296169申请日: 2019-11-25
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公开(公告)号: US12099788B2公开(公告)日: 2024-09-24
- 发明人: Raju Salahuddin , Rahul Dutta , Kevin Tshun Chuan Chai , Ashish James , Chuan Sheng Foo , Zeng Zeng , Savitha Ramasamy , Vijay Ramaseshan Chandrasekhar
- 申请人: Agency for Science, Technology and Research
- 申请人地址: SG Singapore
- 专利权人: Agency for Science, Technology and Research
- 当前专利权人: Agency for Science, Technology and Research
- 当前专利权人地址: SG Singapore
- 代理机构: Shackelford, McKinley & Norton, LLP
- 优先权: SG 201810573V 2018.11.26
- 国际申请: PCT/SG2019/050575 2019.11.25
- 国际公布: WO2020/112023A 2020.06.04
- 进入国家日期: 2021-05-21
- 主分类号: G06F30/27
- IPC分类号: G06F30/27 ; G06F30/31 ; G06N20/00
摘要:
There is provided a method of predicting performance in electronic design based on machine learning using at least one processor, the method including: providing a first machine learning model configured to predict performance data for an electronic system based on a set of input design parameters for the electronic system; providing a second machine learning model configured to generate a new set of parameter values for the set of input design parameters for the electronic system based on a desired performance data provided for the electronic system; generating, using the second machine learning model, the new set of parameter values for the set of input design parameters for the electronic system based on the desired performance data provided for the electronic system; evaluating the set of input design parameters having the new set of parameter values for the electronic system to obtain an evaluated performance data associated with the set of input design parameters having the new set of parameter values; generating a new set of training data based on the set of input design parameters having the new set of parameter values and the evaluated performance data associated with the set of input design parameters having the new set of parameter values; and training the first machine learning model based on at least the new set of training data. There is also provided a corresponding system for predicting performance in electronic design based on machine learning.
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