Method and system for generating training data for a machine learning model for predicting performance in electronic design

Patent metadata

Field	Value
Publication number	US-2022027536-A1
Application number	US-201917296657-A
Country	US
Kind code	A1
Filing date	Nov 26, 2019
Priority date	Nov 26, 2018
Publication date	Jan 27, 2022
Grant date	—

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.

First claim

What is claimed is:

1 . A method of generating training data for a machine learning model for predicting performance in electronic design using at least one processor, the method comprising:
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.

2 . The method according to claim 1 , wherein said generating a first set of training data comprises:
perturbing the first set of input design parameters using the electronic design automation tool to obtain a first set of output performance parameters associated with the first set of input design parameters; and forming first labeled data based on the first set of input design parameters and the first set of output performance parameters.

3 . The method according to claim 2 , wherein the first covariance information comprises a plurality of covariance parameters, each covariance parameter being associated with a respective data pair of an input design parameter of the first set of input design parameters and an output performance parameter of the first set of output performance parameter.

4 . The method according to claim 3 , wherein said each covariance parameter is based on a Pearson correlation coefficient associated with the respective data pair.

5 . The method according to claim 3 , wherein the first covariance information is a first covariance matrix comprising the plurality of covariance parameters as elements therein.

6 . The method according to claim 3 , wherein said determining a second set of input design parameters comprises selecting each input design parameter of the first set of input design parameters having a parameter value that satisfies a first predetermined threshold condition.

7 . The method according to claim 6 , wherein the parameter value of said each input design parameter ranges from −1 to 1, and the first predetermined threshold condition is an absolute parameter value of about 0.5 or greater.

8 . The method according to claim 6 , wherein said generating a second set of training data comprises:
perturbing the second set of input design parameters using the electronic design automation tool to obtain a second set of output performance parameters associated with the second set of input design parameters; and forming second labeled data based on the second set of input design parameters and the second set of output performance parameters.

9 . The method according to claim 1 , configured to generate the training data iteratively in a plurality of iterations, comprising a first iteration and one or more subsequent iterations,
wherein the first iteration comprises:
said generating a first covariance information associated with the first set of input design parameters based on the first set of training data;
said determining a second set of input design parameters based on the first covariance information; and
said generating a second set of training data based on the second set of input design parameters using the electronic design automation tool, and
wherein in each of the one or more subsequent iterations, the subsequent iteration comprises:
generating a further covariance information associated with the set of input design parameters obtained in the immediately previous iteration based on at least the set of training data generated at the immediately previous iteration;
determining a further set of input design parameters based on the further convariance information; and
generating a further set of training data based on the further set of input design parameters and the electronic design automation tool.

10 . The method according to claim 9 , wherein the method continues from a current iteration to a subsequent iteration of the plurality of iterations until the further covariance information is determined to satisfy a predetermined consistency condition.

11 . A system for generating training data for a machine learning model for predicting performance in electronic design, the system comprising:
a memory; and at least one processor communicatively coupled to the memory and configured to: generate a first set of training data based on a first set of input design parameters and an electronic design automation tool; generate a first covariance information associated with the first set of input design parameters based on the first set of training data; determine a second set of input design parameters based on the first covariance information; and generate a second set of training data based on the second set of input design parameters and the electronic design automation tool.

12 . The system according to claim 11 , wherein said generate a first set of training data comprises:
perturbing the first set of input design parameters using the electronic design automation tool to obtain a first set of output performance parameters associated with the first set of input design parameters; and forming first labeled data based on the first set of input design parameters and the first set of output performance parameters.

13 . The system according to claim 12 , wherein the first covariance information comprises a plurality of covariance parameters, each covariance parameter being associated with a respective data pair of an input design parameter of the first set of input design parameters and an output performance parameter of the first set of output performance parameter.

14 . The system according to claim 13 , wherein said each covariance parameter is based on a Pearson correlation coefficient associated with the respective data pair.

15 . The system according to claim 13 , wherein the first covariance information is a first covariance matrix comprising the plurality of covariance parameters as elements therein.

16 . The system according to claim 13 , wherein said determine a second set of input design parameters comprises selecting each input design parameter of the first set of input design parameters having a parameter value that satisfies a first predetermined threshold condition.

17 . The system according to claim 16 , wherein the parameter value of said each input design parameter ranges from −1 to 1, and the first predetermined threshold condition is an absolute parameter value of about 0.5 or greater.

18 . The system according to claim 16 , wherein said generate a second set of training data comprises:
perturbing the second set of input design parameters using the electronic design automation tool to obtain a second set of output performance parameters associated with the second set of input design parameters; and forming second labeled data based on the second set of input design parameters and the second set of output performance parameters.

19 . The system according to claim 11 , wherein the at least one processor is configured to generate the training data iteratively in a plurality of iterations, comprising a first iteration and one or more subsequent iterations,
wherein the first iteration comprises:
said generate a first covariance information associated with the first set of input design parameters based on the first set of training data;
said deter

Assignees

• Agency Science Tech & Res

Inventors

• Dutta Rahul
• Salahuddin Raju
• Chai Kevin Tshun Chuan

Classifications

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F30/30

  Circuit design · CPC title