公开(公告)号：US20230409876A1

公开(公告)日：2023-12-21

申请号：US17845543

申请日：2022-06-21

Applicant: NVIDIA Corporation

Inventor： Vibhor Agrawal ,  Tamar Viclizki ,  Vadim Gechman

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to predict a probability of an error in processing units, such as those of a data center. In at least one embodiment, the probability of an error occurring in a processing unit is identified using a machine learning model trained using one or more previously trained machine learning models, in which the machine learning model is smaller than the previously trained machine learning models.

公开(公告)号：US20240406058A1

公开(公告)日：2024-12-05

申请号：US18629132

申请日：2024-04-08

Applicant: Nvidia Corporation

Inventor： Elad Alon ,  Eitan Zahavi ,  Gaby Diengott ,  Shie Mannor ,  Vadim Gechman

IPC: H04L41/0659 ,  H04L41/147 ,  H04L43/06 ,  H04L43/0811

Abstract: A network monitor may execute, or communicate with, one or more stored machine learning models that are trained to predict a failure probability for one or more ports and/or links within a network fabric. Systems and methods may monitor a set of ports and/or links to generate predictions for failure probabilities using a first trained model and low frequency telemetry data. For a subset of ports and/or links with failure probabilities exceeding a first threshold, high speed telemetry data may be used by a second trained model to generate predictions for failure probabilities for the subset of ports. Suspicious ports may then be isolated and undergo various remediation and/or monitoring actions prior to de-isolating the isolated ports.

公开(公告)号：US20240394130A1

公开(公告)日：2024-11-28

申请号：US18794219

申请日：2024-08-05

Applicant: NVIDIA Corporation

Inventor： Tamar Viclizki ,  Fay Wang ,  Divyansh Jain ,  Avighan Majumder ,  Vadim Gechman ,  Vibhor Agrawal

IPC: G06F11/00 ,  G06N20/20

Abstract: Apparatuses, systems, and techniques to predict a probability of an error or anomay in processing units, such as those of a data center. In at least one embodiment, the probability of an error occuring in a proccessing unit is identified using multiple trained machine learning models, in which the trained machine learning models each outputs, for example, the probability of an error occuring within a different predetermined time period.

公开(公告)号：US12055995B2

公开(公告)日：2024-08-06

申请号：US17683191

申请日：2022-02-28

Applicant: NVIDIA Corporation

Inventor： Tamar Viclizki ,  Fay Wang ,  Divyansh Jain ,  Avighan Majumder ,  Vadim Gechman ,  Vibhor Agrawal

IPC: G06F11/00 ,  G06N20/20

CPC classification number: G06F11/004 ,  G06N20/20 ,  G06F2201/86

Abstract: Apparatuses, systems, and techniques to predict a probability of an error or anomaly in processing units, such as those of a data center. In at least one embodiment, the probability of an error occurring in a processing unit is identified using multiple trained machine learning models, in which the trained machine learning models each outputs, for example, the probability of an error occurring within a different predetermined time period.

公开(公告)号：US20230297453A1

公开(公告)日：2023-09-21

申请号：US17683191

申请日：2022-02-28

Applicant: NVIDIA Corporation

Inventor： Tamar Viclizki ,  Fay Wang ,  Divyansh Jain ,  Avighan Majumder ,  Vadim Gechman ,  Vibhor Agrawal

IPC: G06F11/00 ,  G06N20/20

CPC classification number: G06F11/004 ,  G06N20/20 ,  G06F2201/86

Abstract: Apparatuses, systems, and techniques to predict a probability of an error or anomay in processing units, such as those of a data center. In at least one embodiment, the probability of an error occuring in a proccessing unit is identified using multiple trained machine learning models, in which the trained machine learning models each outputs, for example, the probability of an error occuring within a different predetermined time period.