Back to results
US 2023/0409876 A1Published application
Automatic error prediction for processing nodes of data centers using neural networks
AssigneeNVIDIA Corporation
InventorsVibhor Agrawal, Tamar Viclizki, Vadim Gechman
Life of this patent
Estimating the term…
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.
Claims22
A method comprising:
- receiving first telemetry data corresponding to a first processing device type;
- and
- computing, using a first machine learning model and based at least in part on the first telemetry data corresponding to one or more first processing devices associated with the first processing device type, one or more error predictions corresponding to the one or more first processing devices,
- wherein one or more parameters of the first machine learning model having been updated from one or more outputs generated using a second machine learning model based at least in part on second telemetry data corresponding to the first processing device type, the second machine learning model being trained using historical telemetry data comprising telemetry data corresponding to a plurality of processing device types that comprises at least the first processing device type and at least one other processing device type.
1 more element
A processor comprising processing circuitry to:
- receive historical telemetry data corresponding to one or more devices of a device type;
- generate, based at least in part on an output produced using a first machine learning model trained to generate one or more first error predictions corresponding to the device type, one or more second error predictions using a second machine learning model and corresponding to the device type, wherein the one or more second error predictions are generated using the second machine learning model further based at least in part on (i) a subset of the historical telemetry data and (ii) a subset of the one or more first error predictions of the first machine learning model, the subset of the one or more first error predictions generated using the first machine learning model based at least in part on the subset of the historical telemetry data.
A system comprising:
- one or more processing units to generate, using one or more machine learning models and based at least in part on telemetry data corresponding to one or more first devices of a device type, one or more error predictions corresponding to the one or more first devices, the one or more machine learning models being trained, at least in part, by comparing one or more first outputs of the one or more machine learning models to one or more second outputs of one or more trained machine learning models, the one or more first outputs and the one or more second outputs generated using a same training telemetry data corresponding to one or more second devices of the device type.
Dependencies are inferred from the claim text.