公开(公告)号：US11651129B2

公开(公告)日：2023-05-16

申请号：US17520437

申请日：2021-11-05

Applicant: Synopsys, Inc.

Inventor： Chaofan Wang ,  Vaibhav Jain ,  Shekaripuram Venkatesh ,  Solaiman Rahim

IPC: G06F30/30 ,  G06F30/33 ,  G06F30/27 ,  G06F30/337 ,  G06F30/333 ,  G06N3/02 ,  G06N3/08 ,  G06F119/06 ,  G06F18/2413 ,  G06N3/045

CPC classification number: G06F30/33 ,  G06F30/27 ,  G06F18/24137 ,  G06F30/30 ,  G06F30/333 ,  G06F30/337 ,  G06F2119/06 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08

Abstract: A method includes generating a plurality of vector sequences based on input signals of an electric circuit design and encoding the plurality of vector sequences. The method also includes clustering the plurality of encoded vector sequences into a plurality of clusters and selecting a set of encoded vector sequences from the plurality of clusters. The method further includes selecting a first set of vector sequences corresponding to the selected set of encoded vector sequences, selecting a second set of vector sequences from the plurality of vector sequences not in the first set of encoded vector sequences, and training, by a processing device, a machine learning model to predict power consumption using the first and second sets of vector sequences.

公开(公告)号：US12124780B2

公开(公告)日：2024-10-22

申请号：US17520438

申请日：2021-11-05

Applicant: Synopsys, Inc.

Inventor： Chaofan Wang ,  Vaibhav Jain ,  Shekaripuram Venkatesh ,  Solaiman Rahim

IPC: G06F30/337 ,  G06F30/27 ,  G06F30/33 ,  G06F18/2413 ,  G06F30/30 ,  G06F119/06 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06F30/33 ,  G06F30/27 ,  G06F18/24137 ,  G06F30/30 ,  G06F30/337 ,  G06F2119/06 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08

Abstract: A method includes generating a plurality of input vectors based on input signals to an electric circuit, selecting a subset of the plurality of input vectors, and determining a plurality of datapoints based on the selected subset of the plurality of input vectors. Each datapoint of the plurality of datapoints indicates a power consumption of the electric circuit corresponding to an input vector of the selected subset of the input vectors. The method also includes generating, by a processor, a plurality of vector sequences based on the selected subset of the plurality of input vectors. Each vector sequence of the plurality of vector sequences includes a portion of the selected subset of the plurality of input vectors arranged chronologically. The method further includes training a machine learning model based on a first subset of the plurality of vector sequences and a corresponding first subset of the plurality of datapoints.