Mellanox Technologies, Ltd.

Gal Chechik

Shie Mannor

Dotan David Levi

Dror Porat

Assaf Weissman

Ohad Markus

Eyal Frishman

Assaf Hallak

Eshed Ram

Vadim Gechman

Gaby Vanesa Diengott

Tamar Viclizki

David Slama

Samir Deeb

A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Content-aware, machine-learning-based rate control

A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Quality-metric-agnostic rate control

A method for data-center management includes, in a data center including multiple components, monitoring a plurality of performance measures of the components. A set of composite metrics is automatically defined, each composite metric including a respective weighted combination of two or more performance measures from among the performance measures. Baseline values are established for the composite metrics. An anomalous deviation is detected of one or more of the composite metrics from the respective baseline values.

Identifying anomalies in a data center using composite metrics and/or machine learning

Growing community of inventors

Ramat Hasharon, Israel

Gal Chechik