公开(公告)号：US20200311544A1

公开(公告)日：2020-10-01

申请号：US16651637

申请日：2017-09-28

Applicant: Agency for Science, Technology and Research

Inventor： Savitha Ramasamy ,  Rajaraman Kanagasabai

IPC: G06N3/08 ,  G06N3/04

Abstract: A method, a computer-readable medium, and an apparatus for feature learning are provided. The apparatus may receive a data sample as an input to a feature learning model. The apparatus may calculate a reconstruction error based on the data sample and a plurality of features of the feature learning model. The apparatus may determine whether the reconstruction error satisfies a first threshold. The apparatus may add a feature into the feature learning model to represent the data sample if the data sample satisfies the first threshold. The apparatus may determine whether the reconstruction error satisfies a second threshold. The apparatus may ignore the data sample if the reconstruction error satisfies the second threshold. The apparatus may update the weights associated with the plurality of features of the feature learning model if the reconstruction error satisfies neither the first threshold nor the second threshold.

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：US11657270B2

公开(公告)日：2023-05-23

申请号：US16651637

申请日：2017-09-28

Applicant: Agency for Science, Technology and Research

Inventor： Savitha Ramasamy ,  Rajaraman Kanagasabai

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: A method, a computer-readable medium, and an apparatus for feature learning are provided. The apparatus may receive a data sample as an input to a feature learning model. The apparatus may calculate a reconstruction error based on the data sample and a plurality of features of the feature learning model. The apparatus may determine whether the reconstruction error satisfies a first threshold. The apparatus may add a feature into the feature learning model to represent the data sample if the data sample satisfies the first threshold. The apparatus may determine whether the reconstruction error satisfies a second threshold. The apparatus may ignore the data sample if the reconstruction error satisfies the second threshold. The apparatus may update the weights associated with the plurality of features of the feature learning model if the reconstruction error satisfies neither the first threshold nor the second threshold.

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.