Intel Corporation

Tahi Hollander

James G Hermerding, Ii

Lev Faivishevsky

Amitai Armon

Muhammad Abozaed

Sudheer Nair

Ashwin Muppalla

Avishai Wagner

Gilad Olswang

Moran Peri

Patrick Kam-Shing Leung

Ido Karavany

Sanket Save

Moty Fania

Subash Sudireddy

William Freelove

A mechanism is described for facilitating visual anomaly detection without reference in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to select a frame from a sequence of multiple frames associated with a video stream captured by a camera, and dynamically compute a frame confidence score for the frame based on frame training data associated with frame. The one or more processors are further to detect one or more anomalies in the frame when the frame confidence score is less than a frame confidence threshold associated with the frame, where detecting includes dynamically comparing the frame confidence score with the frame confidence threshold through inference using frame field data and the frame training data.

Visual anomaly detection without reference in graphics computing environments

In one embodiment, a method receives data regarding processing of a workload by a processor. The data is input into a prediction engine configured to classify the data into a plurality of workload classifications. Each workload classification describes different temporal behavior of the workload. Then, the method outputs a prediction for at least one of the plurality of workload classifications, wherein the prediction is used to control performance of the processor in an upcoming period of time.

Classification prediction of workload thermal behavior

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Tel Aviv, Israel

Tahi Hollander