公开(公告)号：US20220027536A1

公开(公告)日：2022-01-27

申请号：US17296657

申请日：2019-11-26

Applicant: Agency for Science, Technology and Research

Inventor： Rahul Dutta ,  Raju Salahuddin ,  Kevin Tshun Chuan Chai

IPC: G06F30/27 ,  G06N20/00

Abstract: There is provided a method of generating training data for a machine learning model for predicting performance in electronic design using at least one processor, the method including: generating a first set of training data based on a first set of input design parameters and an electronic design automation tool; generating a first covariance information associated with the first set of input design parameters based on the first set of training data; determining a second set of input design parameters based on the first covariance information; and generating a second set of training data based on the second set of input design parameters and the electronic design automation tool. There is also provided a corresponding system for generating training data for a machine learning model for predicting performance in electronic design.

公开(公告)号：US12099788B2

公开(公告)日：2024-09-24

申请号：US17296169

申请日：2019-11-25

Applicant: Agency for Science, Technology and Research

Inventor： Raju Salahuddin ,  Rahul Dutta ,  Kevin Tshun Chuan Chai ,  Ashish James ,  Chuan Sheng Foo ,  Zeng Zeng ,  Savitha Ramasamy ,  Vijay Ramaseshan Chandrasekhar

IPC: G06F30/27 ,  G06F30/31 ,  G06N20/00

CPC classification number: G06F30/27 ,  G06F30/31 ,  G06N20/00

Abstract: 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.

公开(公告)号：US20220004900A1

公开(公告)日：2022-01-06

申请号：US17296169

申请日：2019-11-25

Applicant: Agency for Science, Technology and Research

Inventor： Raju Salahuddin ,  Rahul Dutta ,  Kevin Tshun Chuan Chai ,  Ashish James ,  Chuan Sheng Foo ,  Zeng Zeng ,  Savitha Ramasamy ,  Vijay Ramaseshan Chandrasekhar

IPC: G06N5/04 ,  G06N20/00 ,  G06F30/31

Abstract: 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.