Nvidia Corporation

Ashok Srinivasan

Ram Ganapathi

Mohammed Abdulla Yousuf

Gokul Ryan Santhirakumaran

Mike Truog

T Y Chan

David Nister

Edward Anthony Hutchins

Michael J M Toksvig

Gary D Hicok

Tyson J Bergland

Karl Friedrich Greb

Justin Michael Mahan

Jonathan Sweedler

John George Mathieson

Clement Farabet

James T Battle

Michael Alan Ditty

Michael Truog

Daniel Stewart Perrin

Kevin Flory

Tai-Yuen Chan

Dan Hettena

TY Chan

In various examples, a voltage monitor may determine whether the voltage supplied to at least one component of a computing system is safe using two sets of thresholds&#x2014;e.g., a high-frequency over-voltage (OV) threshold, a high-frequency under-voltage (UV) threshold, a low-frequency OV threshold, and a low-frequency UV threshold. A high-frequency voltage error detector may compare the supplied or input voltage to the high-frequency OV and UV thresholds and a low-frequency voltage error detector that may filter the supplied voltage to remove or reduce noise and then may compare the filtered voltage to the low-frequency OV and UV thresholds. Upon detecting a voltage error, a safety monitor may cause a change to an operating state of the at least one component.

Voltage monitoring over multiple frequency ranges for autonomous machine applications

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 a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed. Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL-D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

Method of using a single controller (ECU) for a fault-tolerant/fail-operational self-driving system

In a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed. Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

An apparatus is disclosed. The apparatus comprises an instruction mapping table, which includes a plurality of instruction counts and a plurality of instruction pointers each corresponding with one of the instruction counts. Each instruction pointer identifies a next instruction for execution. Further, each instruction count specifies a number of instructions to execute beginning with the next instruction. The apparatus also has a data operation unit adapted to receive a data group and adapted to execute on the received data group the number of instructions specified by a current instruction count of the instruction mapping table beginning with the next instruction identified by a current instruction pointer of the instruction mapping table before proceeding with another data group.

Reducing instruction execution passes of data groups through a data operation unit

Growing community of inventors

Palo Alto, CA, United States of America

Ashok Srinivasan