US 12,299,364 B2
Multi-algorithmic approach to represent highly non-linear high dimensional space
Nikhil Gupta, Plano, TX (US); Timothy W. Fischer, McKinney, TX (US); Ashish Khandelwal, Frisco, TX (US); and Sreenivasan K. Koduri, Dallas, TX (US)
Assigned to TEXAS INSTRUMENTS INCORPORATED, Dallas, TX (US)
Filed by TEXAS INSTRUMENTS INCORPORATED, Dallas, TX (US)
Filed on Apr. 30, 2021, as Appl. No. 17/245,306.
Claims priority of provisional application 63/116,578, filed on Nov. 20, 2020.
Claims priority of provisional application 63/037,385, filed on Jun. 10, 2020.
Prior Publication US 2021/0390234 A1, Dec. 16, 2021
Int. Cl. G06F 30/27 (2020.01); G06F 18/2415 (2023.01); G06F 30/3308 (2020.01); G06F 30/337 (2020.01); G06F 30/367 (2020.01); G06F 30/373 (2020.01); G06F 30/392 (2020.01); G06F 30/398 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06F 111/04 (2020.01); G06F 111/20 (2020.01)
CPC G06F 30/27 (2020.01) [G06F 18/24155 (2023.01); G06F 30/3308 (2020.01); G06F 30/337 (2020.01); G06F 30/367 (2020.01); G06F 30/373 (2020.01); G06F 30/392 (2020.01); G06F 30/398 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06F 2111/04 (2020.01); G06F 2111/20 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
producing a first machine learning (ML) model, including receiving an initial set of parameters, wherein the initial set of parameters represents inputs to the first ML model, the initial set of parameters associated with a sub-circuit;
interacting a first parameter of the initial set of parameters with other parameters of the initial set of parameters to generate a set of interacted parameters;
adding the interacted parameter to the initial set of parameters to generate a candidate set of parameters;
performing a linear regression on parameters of the candidate set of parameters against a set of expected parameter values to determine a predictive value for parameters of the candidate set of parameters;
removing parameters of the candidate set of parameters based on a comparison between the predictive value and a predetermined predictive threshold;
determining an accuracy of the candidate set of parameters based on the set of expected parameter values;
comparing the accuracy of the candidate set of parameters to a predetermined accuracy level;
determining that the accuracy of the candidate set of parameters reaches the predetermined accuracy level;
in response to determining that the accuracy of the candidate set of parameters reaches the predetermined accuracy level, storing the candidate set of parameters as a set of inputs for a first layer of the first ML model for the sub-circuit for a process technology; and
training the first ML model using the candidate set of parameters stored as the set of inputs, thereby producing a trained ML model.