Nvidia Corporation

Vlsi Technology, Inc.

Cirrus Logic Inc.

S3 Incorporated, Inc.

Ylsi Technology, Inc.

Gary D Hicok

Robert Anthony Alfieri

Paul J Sidenblad

Mark A Parris

Scott E Harrow

Michael Brian Cox

David R Evoy

David Nister

Yongmin Kim

Jeffery M Michelsen

Kenneth M Potts

Gregg D Lahti

Clement Farabet

Thomas Alexander

Yong J Lim

David G Reed

Lonnie Calvin Goff

Joseph Alan Thomsen

John R Zedlewski

Gary A Walker

Rev Lebaredian

Ratin Kumar

Ruchi Bhargava

Tony Tamasi

Gordon Grigor

Justin Ebert

Judson A Lehman

Miguel Sainz

Timo Roman

Greg Heinrich

Claire Delaunay

Tony C Tam

Zachary Taylor

Mark Daly

Matthew Campbell

David Auld

Martin Hempel

David Wyatt

Franck Diard

Nicholas Julian Richardson

Franklyn H Story

Ludger Mimberg

David K Cassetti

Laura Elisabeth Simmons

Rahul Gautam Patel

Karl Friedrich Greb

James C Steele

Rambod Jacoby

Mike Nakahara

Ram Ganapathi

Jonathan Sweedler

Matthew Raymond Longnecker

John George Mathieson

Ashok Srinivasan

Barry M Davis

Michael Truog

Michael Alan Ditty

Mohammed Abdulla Yousuf

Kevin Flory

Koichi Eugene Nomura

Mike Truog

Curtis A Beeson

Daniel Stewart Perrin

Bruce E Cairns

Chin-Hsien Shih

Curtis Beeson

Chin Shih

Tai-Yuen Chan

Dan Hettena

Micheal H Zhu

Eric A Hildebrandt

Dale C Penner

TY Chan

Daniel Perrin

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

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Systems and methods for computer-assisted shuttles, buses, robo-taxis, ride-sharing and on-demand vehicles with situational awareness

Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a faster, more reliable, safer, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.

Systems and methods for safe and reliable autonomous vehicles

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 technique for managing processor cores within a multi-core central processing unit (CPU) provides efficient power and resource utilization over a wide workload range. The CPU comprises at least one core designed for low power operation and at least one core designed for high performance operation. For low workloads, the low power core executes the workload. For certain higher workloads, the high performance core executes the workload. For certain other workloads, the low power core and the high performance core both share execution of the workload. This technique advantageously enables efficient processing over a wider range of workloads than conventional systems.

Heterogeneous multiprocessor design for power-efficient and area-efficient computing

In a computer system with multiple processing units, power to one or more of the processing units is turned off while the other processing units remain powered on. The processing unit that is powered off may be a GPU on a graphics adapter card, and power to this GPU is controlled by turning on and off the power supplied through a voltage regulator. With this configuration, power to the GPU on the graphics adapter card can be turned off when it is not in use or when it is being used for graphics processing that another graphics processor can handle.

Method and apparatus for controlling power to a processing unit

A novel network architecture that integrates the functions of an internet protocol (IP) router into a network processing unit (NPU) that resides in a host computer's chipset such that the host computer's resources are perceived as separate network appliances. The NPU appears logically separate from the host computer even though, in one embodiment, it is sharing the same chip.

Method and apparatus for performing network processing functions

Method and apparatus for providing an integrated network of processors

A novel network architecture that integrates the functions of an Internet protocol (IP) router into a network processing unit (NPU) that resides in a host computer's chipset such that the host computer's resources are perceived as separate network appliances. The NPU appears logically separate from the host computer even though, in one embodiment, it is sharing the same chip.

Internet protocol (IP) router residing in a processor chipset

Network processing pipeline chipset for routing and host packet processing

Growing community of inventors

Mesa, AZ, United States of America

Gary D Hicok