Nvidia Corporation

Michael Alan Ditty

David Nister

Gary D Hicok

Karl Friedrich Greb

Jonathan Sweedler

Chung-Hong Lai

John George Mathieson

Ram Ganapathi

Anirban Ghosh

Ashok Srinivasan

Hari Unni Krishnan

Jyotirmaya Swain

Padam Patt Krishnani

Padmanabham Patki

Clement Farabet

Avinash J V

Mohammed Abdulla Yousuf

Michael Truog

Phanikumar Parvatham

Kevin Flory

Shraddha Manohar Gondkar

Daniel Stewart Perrin

Mike Truog

Tai-Yuen Chan

Dan Hettena

Nisha Bhushan

Arpit Gupta

Kiran Kumar Dash

TY Chan

In various examples, methods may include receiving first data transmitted from a second component and second data transmitted from the second component during a first time period. The first data may be transmitted via a first data lane and the second data may be transmitted via a second data lane. The method may include receiving de-skew symbols at an interval from the second component via the first data lane and via the second data lane during the first time period. The method may include compensating for a first skew introduced to a first propagation of the first data across the first data lane and a second skew introduced to a second propagation of the second data across the second data lane using the de-skew symbols received during the first time period.

Dynamic skew realignment over multiple transmission lanes in integrated computing platforms

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, a system includes a memory operating within a first risk level and circuitry operating within a second risk level that indicates more risk than the first risk level. The circuitry reads and/or writes data to a first memory address within the memory, and reads and/or writes an error detection code to a second memory address within the memory.

Transmitting data between regions of varying safety integrity levels in a system on a chip

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San Jose, CA, United States of America

Michael Alan Ditty