Patents by Inventor Clément Farabet
Clément Farabet has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240045426Abstract: 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.Type: ApplicationFiled: May 4, 2023Publication date: February 8, 2024Inventors: Michael Alan DITTY, Gary HICOK, Jonathan SWEEDLER, Clement FARABET, Mohammed Abdulla YOUSUF, Tai-Yuen CHAN, Ram GANAPATHI, Ashok SRINIVASAN, Michael Rod TRUOG, Karl GREB, John George MATHIESON, David NISTER, Kevin FLORY, Daniel PERRIN, Dan HETTENA
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Publication number: 20230176577Abstract: 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.Type: ApplicationFiled: December 7, 2022Publication date: June 8, 2023Inventors: Michael Alan DITTY, Gary HICOK, Jonathan SWEEDLER, Clement FARABET, Mohammed Abdulla YOUSUF, Tai-Yuen CHAN, Ram GANAPATHI, Ashok SRINIVASAN, Michael Rod TRUOG, Karl GREB, John George MATHIESON, David NISTER, Kevin FLORY, Daniel PERRIN, Dan HETTENA
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Patent number: 11644834Abstract: 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.Type: GrantFiled: November 9, 2018Date of Patent: May 9, 2023Assignee: NVIDIA CorporationInventors: Michael Alan Ditty, Gary Hicok, Jonathan Sweedler, Clement Farabet, Mohammed Abdulla Yousuf, Tai-Yuen Chan, Ram Ganapathi, Ashok Srinivasan, Michael Rod Truog, Karl Greb, John George Mathieson, David Nister, Kevin Flory, Daniel Perrin, Dan Hettena
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Publication number: 20230004801Abstract: 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.Type: ApplicationFiled: August 30, 2022Publication date: January 5, 2023Inventors: Clement Farabet, John Zedlewski, Zachary Taylor, Greg Heinrich, Claire Delaunay, Mark Daly, Matthew Campbell, Curtis Beeson, Gary Hicok, Michael Cox, Rev Lebaredian, Tony Tamasi, David Auld
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Patent number: 11436484Abstract: 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.Type: GrantFiled: March 27, 2019Date of Patent: September 6, 2022Assignee: NVIDIA CorporationInventors: Clement Farabet, John Zedlewski, Zachary Taylor, Greg Heinrich, Claire Delaunay, Mark Daly, Matthew Campbell, Curtis Beeson, Gary Hicok, Michael Cox, Rev Lebaredian, Tony Tamasi, David Auld
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Publication number: 20220180125Abstract: Apparatuses, systems, and techniques to train a machine-learned model. In at least one embodiment, a plurality of training clients each obtain an exclusive right to update a model in turn, and each client trains said model with training data not accessible to other training clients.Type: ApplicationFiled: December 22, 2020Publication date: June 9, 2022Inventors: Yichun Shen, Siyi Li, Yuhong Wen, Clement Farabet
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Publication number: 20190303759Abstract: 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.Type: ApplicationFiled: March 27, 2019Publication date: October 3, 2019Inventors: Clement Farabet, John Zedlewski, Zachary Taylor, Greg Heinrich, Claire Delaunay, Mark Daly, Matthew Campbell, Curtis Beeson, Gary Hicok, Michael Cox, Rev Lebaredian, Tony Tamasi, David Auld
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Publication number: 20190258251Abstract: 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.Type: ApplicationFiled: November 9, 2018Publication date: August 22, 2019Inventors: Michael Alan DITTY, Gary HICOK, Jonathan SWEEDLER, Clement FARABET, Mohammed Abdulla YOUSUF, Tai-Yuen CHAN, Ram GANAPATHI, Ashok SRINIVASAN, Michael Rod TRUOG, Karl GREB, John George MATHIESON, David Nister, Kevin Flory, Daniel Perrin, Dan Hettena
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Patent number: 10078620Abstract: A processor includes a plurality of processing tiles, wherein each tile is configured at runtime to perforin a configurable operation. A first subset of tiles are configured to perform in a pipeline a first plurality of configurable operations in parallel. A second subset of tiles are configured to perform a second plurality of configurable operations in parallel with the first plurality of configurable operations. The process also includes a multi-port memory access module operably connected to the plurality of tiles via a data bus configured to control access to a memory and to provide data to two or more processing tiles simultaneously. The processor also includes a controller operably connected to the plurality of tiles and the multi-port memory access module via a runtime bus. The processor configures the tiles and the multi-port memory access module to execute a computation.Type: GrantFiled: May 24, 2012Date of Patent: September 18, 2018Assignee: NEW YORK UNIVERSITYInventors: Clément Farabet, Yann LeCun
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Publication number: 20120303932Abstract: A processor includes a plurality of processing tiles, wherein each tile is configured at runtime to perform a configurable operation. A first subset of tiles are configured to perform in a pipeline a first plurality of configurable operations in parallel. A second subset of tiles are configured to perform a second plurality of configurable operations in parallel with the first plurality of configurable operations. The process also includes a multi-port memory access module operably connected to the plurality of tiles via a data bus configured to control access to a memory and to provide data to two or more processing tiles simultaneously. The processor also includes a controller operably connected to the plurality of tiles and the multi-port memory access module via a runtime bus. The processor configures the tiles and the multi-port memory access module to execute a computation.Type: ApplicationFiled: May 24, 2012Publication date: November 29, 2012Inventors: Clément Farabet, Yann LeCun