Deep learning testability analysis with graph convolutional networks

Invention Grant

US10657306B1 Deep learning testability analysis with graph convolutional networks 有权

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Patent Title: Deep learning testability analysis with graph convolutional networks
Application No.: US16520688

Application Date: 2019-07-24
Publication No.: US10657306B1

Publication Date: 2020-05-19
Inventor: Yuzhe Ma , Haoxing Ren , Brucek Khailany , Harbinder Sikka , Lijuan Luo , Karthikeyan Natarajan
Applicant: NVIDIA Corp.
Applicant Address: US CA Santa Clara
Assignee: NVIDIA Corp.
Current Assignee: NVIDIA Corp.
Current Assignee Address: US CA Santa Clara
Agency: Rowan TELS LLC
Main IPC: G06F17/50
IPC: G06F17/50 ; G06F30/327 ; G06F30/367 ; G06F30/3323 ; G06F30/323 ; G06F30/398 ; G06F30/3308

Deep learning testability analysis with graph convolutional networks

Abstract:

Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

Public/Granted literature

US20200151288A1 Deep Learning Testability Analysis with Graph Convolutional Networks Public/Granted day:2020-05-14

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