Nvidia Corporation

Navidia Corporation

Tony Tamasi

Franck Diard

Rev Lebaredian

Xun Wang

John F Spitzer

David Wyatt

Gary D Hicok

Michael Brian Cox

John R Zedlewski

Greg Heinrich

Clement Farabet

Claire Delaunay

Mark Daly

Matthew Campbell

David Auld

Zachary Taylor

Emmett Michael Kilgariff

Brad W Simeral

Gerrit Ary Slavenburg

Stephen Donald Lew

David G Reed

Abraham Benjamin De Waal

Rouslan Lyubomirov Dimitrov

Hassane S Azar

Thomas Francis Fox

Ross Cunniff

Jen-Hsun Huang

Robert Jan Schutten

David Matthew Stears

Bojan Skaljak

Bruce R Intihar

Alok Ahuja

Jensen Huang

Laurence Harrison

Ajay Kamalvanshi

Curtis A Beeson

Curtis Beeson

Andrew Edelsten

Tom Petersen

Paul Van Der Kouwe

Robert Osborne

Traditionally, a software application is developed, tested, and then published for use by end users. Any subsequent update made to the software application is generally in the form of a human programmed modification made to the code in the software application itself, and further only becomes usable once tested, published, and installed by end users having the previous version of the software application. This typical software application lifecycle causes delays in not only generating improvements to software applications, but also to those improvements being made accessible to end users. To help avoid these delays and improve performance of software applications, deep learning models may be made accessible to the software applications for use in providing inferenced data to the software applications, which the software applications may then use as desired. These deep learning models can furthermore be improved independently of the software applications using manual and/or automated processes.

Automatic dataset creation using software tags

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

Training, testing, and verifying autonomous machines using simulated environments

Embodiments of the present invention provide a novel solution that uses subjective end-user input to generate optimal image quality settings for an application. Embodiments of the present invention enable end-users to rank and/or select various adjustable application parameter settings in a manner that allows them to specify which application parameters and/or settings are most desirable to them for a given application. Based on the feedback received from end-users, embodiments of the present invention may generate optimal settings for whatever performance level the end-user desires. Furthermore, embodiments of the present invention may generate optimal settings that may be benchmarked either on a server farm or on an end-user's client device.

Method and system for customizing optimal settings using end-user preferences

Novel solutions are provided for consistent Quality of Service in cloud gaming system that adaptively and dynamically compensate for poor network conditions by moderating rendered frame rates using frame rate capping to optimize for network latency savings (or surplus). In further embodiments, the encoding/sent frame rate to the client can also be managed in addition, or as an alternative to capping the rendered frame rates. The claimed embodiments not only maintain a constant Quality of Service (QoS) for the user, but may also be employed to leverage higher-performing networks to reduce operational costs.

Network adaptive latency reduction through frame rate control

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment&#x2014;in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack&#x2014;to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

A computer application streaming system includes an optimization unit coupled to a streaming device to determine streaming optimal playable settings for a remote user device corresponding to a selected computer application and a sending unit coupled to the optimization unit to manage streaming of the streaming optimal playable settings over a network connected to the remote user device. A receiving unit is coupled to the network to recover the streaming optimal playable settings for application to the remote user device when employing the selected computer application. An optional feedback unit is coupled to the remote user device to provide remote information over the network for modifying the streaming optimal playable settings, and an optional update unit is coupled to the streaming device to manage modification of the streaming optimal playable settings as directed by the remote information. A method of streaming a computer application is also provided.

Optimal settings for application streaming

A system, method, and computer program product are provided for a dynamic display refresh. In use, a state of a display device is identified in which an entirety of an image frame is currently displayed by the display device. In response to the identification of the state, it is determined whether an entirety of a next image frame to be displayed has been rendered to memory. The next image frame is transmitted to the display device for display thereof, when it is determined that the entirety of the next image frame to be displayed has been rendered to the memory. Further, a refresh of the display device is delayed, when it is determined that the entirety of the next image frame to be displayed has not been rendered to the memory.

System, method, and computer program product for providing a dynamic display refresh

A method of displaying graphics data is described. The method involves accessing the graphics data in a memory subsystem associated with one graphics subsystem. The graphics data is transmitted to a second graphics subsystem, where it is displayed on a monitor coupled to the second graphics subsystem.

Hybrid graphic display

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Los Gatos, CA, United States of America

Tony Tamasi