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Gordon F Grigor, 544234 22Nd St, San Francisco, CA 94114

Known as:
Grigor Gordon Gordan Girgor Girgor Gordan
Related to:
David H Park 71

Gordon Grigor Phones & Addresses

4234 22Nd St, San Francisco, CA 94114    415-3418445   

1045 Mission St, San Francisco, CA 94103    415-4317373   

Grass Valley, CA   

4400 The Woods Dr, San Jose, CA 95136   

Social networks

Gordon F Grigor

Linkedin

Work

Company: Nvidia May 2016 to Oct 2019 Position: Senior director worldwide customer engineering, mobile

Education

Degree: Bachelors, Bachelor of Science School / High School: University of Toronto 1990 to 1994 Specialities: Mathematics, Computer Science

Skills

Device Drivers • Mobile Devices • Software Engineering • Software Development • Android • Embedded Systems • Processors • Linux • Soc • Hardware • Embedded Software • Semiconductors • Product Management • Asic • Consumer Electronics • Wireless • Distributed Systems • Product Marketing • System Architecture • Go To Market Strategy • Management • Cloud Computing • C • Product Development • Fpga • Software Design • Ic • Perl • Program Management • Digital Signal Processors • Mobile Applications • Cross Functional Team Leadership • Algorithms • Firmware • System on A Chip • C++ • Software Project Management • Digital Media • Agile Methodologies • Architecture • High Performance Computing • Engineering Management • Strategic Partnerships • Field Programmable Gate Arrays • Computer Hardware • Adas • Autonomous Vehicles • Computer Vision • Deep Learning

Industries

Computer Hardware
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Gordon Grigor resumes & CV records

Resumes

Gordon Grigor Photo 17

Gordon Grigor

Location:
San Francisco, CA
Industry:
Computer Hardware
Work:
Nvidia May 2016 - Oct 2019
Senior Director Worldwide Customer Engineering, Mobile Marvell Semiconductor Jan 2011 - Apr 2012
Vp, Mobile Amd May 2002 - Apr 2006
Architect, Imageon Software Vivace Networks Sep 2000 - May 2002
Engineer, Software Amd May 1994 - Sep 2000
Architect, Radeon Software
Education:
University of Toronto 1990 - 1994
Bachelors, Bachelor of Science, Mathematics, Computer Science
Skills:
Device Drivers, Mobile Devices, Software Engineering, Software Development, Android, Embedded Systems, Processors, Linux, Soc, Hardware, Embedded Software, Semiconductors, Product Management, Asic, Consumer Electronics, Wireless, Distributed Systems, Product Marketing, System Architecture, Go To Market Strategy, Management, Cloud Computing, C, Product Development, Fpga, Software Design, Ic, Perl, Program Management, Digital Signal Processors, Mobile Applications, Cross Functional Team Leadership, Algorithms, Firmware, System on A Chip, C++, Software Project Management, Digital Media, Agile Methodologies, Architecture, High Performance Computing, Engineering Management, Strategic Partnerships, Field Programmable Gate Arrays, Computer Hardware, Adas, Autonomous Vehicles, Computer Vision, Deep Learning

Publications & IP owners

Us Patents

Handling Of Secure Storage Key In Always On Domain

US Patent:
2009020, Aug 13, 2009
Filed:
Feb 11, 2008
Appl. No.:
12/029463
Inventors:
Michael Cox - Menlo Park CA, US
Gordon Grigor - San Francisco CA, US
Phillip Smith - Sunnyvale CA, US
Parthasarathy Sriram - Los Altos CA, US
Assignee:
NVIDIA CORPORATION - Santa Clara CA
International Classification:
G06F 21/00
H04L 9/28
G06F 9/445
US Classification:
713 2, 380 44, 713189
Abstract:
Techniques for handling a secure storage key maintain the key in an always on domain and restore the key to the encryption/decryption engine when the engine is turned back on. The secure storage key however is only accessible by the boot loader code, which provides a secure chain of trust. In addition, the techniques allow the secure storage key to be updated.

Confidential Information Protection System And Method

US Patent:
2009020, Aug 13, 2009
Filed:
Feb 11, 2008
Appl. No.:
12/069713
Inventors:
Parthasarathy Sriram - Los Altos CA, US
Gordon Grigor - San Francisco CA, US
Shu-Jen Fang - Cupertino CA, US
International Classification:
G06F 21/24
US Classification:
726 30
Abstract:
Efficient and effective permission confidential information protection systems and methods are described. The secure information protection systems and methods facilitate storage of confidential information in a manner safe from rogue software access. In one embodiment, a confidential information protection method is implemented in hardware and facilitates protection against software and/or Operating System hacks. In one exemplary implementation, a confidential information protection method includes setting a permission sticky bit flag to a default state upon system set up. The permission sticky bit flag access permission indication is adjusted at system reset in accordance with an initial application instruction. Access to the confidential information is restricted in accordance with the permission sticky bit and the permission sticky bit is protected from adjustments attempting to violate the permission indication. For example, another software application can not access or alter confidential information (e.g., an encryption key, initialization vector, etc.) if a permission sticky bit is designated as the highest security rating (e.g., disabling read permission and write permission until system reset).

Secure Update Of Boot Image Without Knowledge Of Secure Key

US Patent:
2010007, Mar 18, 2010
Filed:
Feb 11, 2008
Appl. No.:
12/029467
Inventors:
Gordon Grigor - San Francisco CA, US
Phillip Norman Smith - Sunnyvale CA, US
Assignee:
NVIDIA CORPORATION - Santa Clara CA
International Classification:
G06F 9/24
US Classification:
713 1
Abstract:
Techniques for securely updating a boot image without knowledge of a secure key used to encrypt the boot image.

Systems And Methods For Computer-Assisted Shuttles, Buses, Robo-Taxis, Ride-Sharing And On-Demand Vehicles With Situational Awareness

US Patent:
2022041, Dec 29, 2022
Filed:
Aug 26, 2022
Appl. No.:
17/896825
Inventors:
- Santa Clara CA, US
Michael COX - Menlo Park CA, US
Miguel SAINZ - Palo Alto CA, US
Martin HEMPEL - Mountain View CA, US
Ratin KUMAR - Cupertino CA, US
Timo ROMAN - Uusimaa, FI
Gordon GRIGOR - San Francisco CA, US
David NISTER - Bellevue WA, US
Justin EBERT - Boulder CO, US
Chin-Hsien SHIH - Saratoga CA, US
Tony TAM - Redwood City CA, US
Ruchi BHARGAVA - Redmond WA, US
International Classification:
G05D 1/00
G06Q 50/30
G05B 13/02
G05D 1/02
G06Q 10/02
Abstract:
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.

Real-Time Detection Of Lanes And Boundaries By Autonomous Vehicles

US Patent:
2021022, Jul 22, 2021
Filed:
Apr 5, 2021
Appl. No.:
17/222680
Inventors:
- Santa Clara CA, US
Xin Liu - Pleasanton CA, US
Chia-Chih Chen - San Jose CA, US
Carolina Parada - Boulder CO, US
Davide Onofrio - San Francisco CA, US
Minwoo Park - Cupertino CA, US
Mehdi Sajjadi Mohammadabadi - Santa Clara CA, US
Vijay Chintalapudi - Sunnyvale CA, US
Ozan Tonkal - Munich, DE
John Zedlewski - San Francisco CA, US
Pekka Janis - Uusimaa, FI
Jan Nikolaus Fritsch - Santa Clara CA, US
Gordon Grigor - San Francisco CA, US
Miguel Sainz - Palo Alto CA, US
International Classification:
G06K 9/00
G06K 9/32
G06T 7/10
G05D 1/00
G06N 3/08
G05D 1/02
G06K 9/46
G06K 9/48
G06K 9/62
Abstract:
In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Systems And Methods For Computer-Assisted Shuttles, Buses, Robo-Taxis, Ride-Sharing And On-Demand Vehicles With Situational Awareness

US Patent:
2019026, Aug 29, 2019
Filed:
Feb 26, 2019
Appl. No.:
16/286330
Inventors:
- Santa Clara CA, US
Michael COX - Menlo Park CA, US
Miguel SAINZ - Palo Alto CA, US
Martin HEMPEL - Mountain View CA, US
Ratin KUMAR - Cupertino CA, US
Timo ROMAN - Uusimaa, FI
Gordon GRIGOR - San Francisco CA, US
David NISTER - Bellevue WA, US
Justin EBERT - Lafayette CO, US
Chin SHIH - Saratoga CA, US
Tony TAM - Redwood City CA, US
Ruchi BHARGAVA - Redmond WA, US
International Classification:
G05D 1/00
G06Q 50/30
G05B 13/02
G05D 1/02
Abstract:
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.

Real-Time Detection Of Lanes And Boundaries By Autonomous Vehicles

US Patent:
2019026, Aug 29, 2019
Filed:
Feb 26, 2019
Appl. No.:
16/286329
Inventors:
- San Jose CA, US
Xin Liu - Pleasanton CA, US
Chia-Chih Chen - San Jose CA, US
Carolina Parada - Boulder CO, US
Davide Onofrio - San Francisco CA, US
Minwoo Park - Cupertino CA, US
Mehdi Sajjadi Mohammadabadi - Santa Clara CA, US
Vijay Chintalapudi - Sunnyvale CA, US
Ozan Tonkal - Munich, DE
John Zedlewski - San Francisco CA, US
Pekka Janis - Uusimaa, FI
Jan Nikolaus Fritsch - Santa Clara CA, US
Gordon Grigor - San Francisco CA, US
Miguel Sainz - Palo Alto CA, US
International Classification:
G06K 9/00
G06K 9/32
G05D 1/02
G05D 1/00
G06N 3/08
G06T 7/10
Abstract:
In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Efficient Lossless Compression Of Captured Raw Image Information Systems And Methods

US Patent:
2019008, Mar 14, 2019
Filed:
Jul 27, 2018
Appl. No.:
16/048120
Inventors:
- Santa Clara CA, US
Gordon Grigor - San Francisco CA, US
Vinayak Pore - Pune, IN
Gajanan Bhat - San Jose CA, US
Mohan Nimaje - Pune, IN
Soumen Dey - Pune, IN
Sameer Gumaste - Pune, IN
International Classification:
H04N 19/182
G06T 7/11
H04N 19/186
H04N 19/33
G06N 3/02
Abstract:
Systems and methods for efficient lossless compression of captured raw image information are presented. A method can comprise: receiving raw image data from an image capture device, segregating the pixel data into a base layer portion and an enhanced layer portion, reconfiguring the base layer portion expressed in the first color space values from a raw capture format into a pseudo second color space compression mechanism compatible format, and compressing the reconfigured base layer portion of first color space values. The raw image data can include pixel data are expressed in first color space values. The segregation can be based upon various factors, including a compression benefits analysis of a boundary location between the base layer portion and enhanced layer portion. The reconfiguring the base layer portion can include separating the base layer portion based upon multiple components within the raw data; and forming base layer video frames from the multiple components.

Possible Relatives

Elizabeth V Grigor  Michael P Grigor  Robert R Grigor  Gordon F Johnson  Gordon D Stanard  Gordon E Standish  Gordon L Johnston  Gordon B White  Gordon W Joiner  Gordon Jones 

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