公开(公告)号：US20200160185A1

公开(公告)日：2020-05-21

申请号：US16197986

申请日：2018-11-21

Applicant: NVIDIA CORPORATION

Inventor： Varun Praveen ,  Anil Ubale ,  Parthasarathy Sriram ,  Greg Heinrich ,  Tayfun Gurel

IPC: G06N3/08 ,  G06N3/04

Abstract: Input layers of an element-wise operation in a neural network can be pruned such that the shape (e.g., the height, the width, and the depth) of the pruned layers matches. A pruning engine identifies all of the input layers into the element-wise operation. For each set of corresponding neurons in the input layers, the pruning engine equalizes the metrics associated with the neurons to generate an equalized metric associated with the set. The pruning engine prunes the input layers based on the equalized metrics generated for each unique set of corresponding neurons.

公开(公告)号：US09329671B2

公开(公告)日：2016-05-03

申请号：US13753174

申请日：2013-01-29

Applicant: Nvidia Corporation

Inventor： Greg Heinrich ,  Philippe Guasch

IPC: G06F1/26 ,  G06F1/32 ,  G06F9/48

CPC classification number: G06F1/329 ,  G06F1/3203 ,  G06F1/3293 ,  G06F9/4893 ,  Y02D10/122 ,  Y02D10/24

Abstract: Computer system, method and computer program product for scheduling IPC activities are disclosed. In one embodiment, the computer system includes first processor and second processors that communicate with each other via IPC activities. The second processor may operate in a first mode in which the second processor is able to process IPC activities, or a second mode in which the second processor does not process IPC activities. Processing apparatus associated with the first processor identifies which of the pending IPC activities for communicating from the first processor to the second processor are not real-time sensitive, and schedules the identified IPC activities for communicating from the first processor to the second processor by delaying some of the identified IPC activities to thereby group them together. The grouped IPC activities are scheduled for communicating to the second processor during a period in which the second processor is continuously in the first mode.

Abstract translation: 披露了IPC活动安排的计算机系统，方法和计算机程序产品。 在一个实施例中，计算机系统包括通过IPC活动彼此通信的第一处理器和第二处理器。 第二处理器可以在其中第二处理器能够处理IPC活动的第一模式中操作，或第二模式，其中第二处理器不处理IPC活动。 与第一处理器相关联的处理装置识别用于从第一处理器到第二处理器进行通信的待处理IPC活动中的哪一个不是实时敏感的，并且通过延迟一些从第一处理器到第二处理器的通信来调度所识别的IPC活动 的IPC活动，从而将它们组合在一起。 分组的IPC活动被安排用于在第二处理器连续处于第一模式的时段期间与第二处理器进行通信。

公开(公告)号：US20190303759A1

公开(公告)日：2019-10-03

申请号：US16366875

申请日：2019-03-27

Applicant: NVIDIA Corporation

Inventor： 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

IPC: G06N3/08 ,  G06F9/455 ,  G06N20/00

Abstract: 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.

公开(公告)号：US20140342727A1

公开(公告)日：2014-11-20

申请号：US13894704

申请日：2013-05-15

Applicant: Nvidia Corporation

Inventor： Greg Heinrich ,  Robert Riglar

IPC: H04W52/02

CPC classification number: H04W52/0229 ,  Y02D70/1262

Abstract: In an aspect there is provided a method of moving a processor of a mobile device from a low-power state for conserving power to an active mode for processing signals. The mobile device is configured to receive regularly scheduled signals. The method comprises, for each of multiple operating states of the mobile device determining a restore time associated with the operating state of the mobile device and storing each determined restore time in association with its operating state. The method further comprises detecting a current operating state of the mobile device and using the determined restore time for that state to set a trigger time to control movement of the processor of the mobile device to enter the active mode from the low-power mode in time to process the scheduled signals.

Abstract translation: 在一方面，提供了一种将移动设备的处理器从低功率状态移动以将功率节省到用于处理信号的活动模式的方法。 移动设备被配置为接收定时调度的信号。 该方法包括针对移动设备的多个操作状态中的每一个确定与移动设备的操作状态相关联的恢复时间，并且与其操作状态相关联地存储每个确定的恢复时间。 该方法还包括检测移动设备的当前操作状态并且使用所确定的恢复时间来设置触发时间，以控制移动设备的处理器从低功率模式中进入活动模式的时间 处理调度信号。

公开(公告)号：US20250111216A1

公开(公告)日：2025-04-03

申请号：US18980252

申请日：2024-12-13

Applicant: NVIDIA Corporation

Inventor： 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

IPC: G06N3/063 ,  G06F9/455 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

Abstract: 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.

公开(公告)号：US12182694B2

公开(公告)日：2024-12-31

申请号：US17898887

申请日：2022-08-30

Applicant: NVIDIA Corporation

Inventor： 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

IPC: G06F9/455 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

Abstract: 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.

公开(公告)号：US20230004801A1

公开(公告)日：2023-01-05

申请号：US17898887

申请日：2022-08-30

Applicant: NVIDIA Corporation

Inventor： 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

IPC: G06N3/08 ,  G06N20/00 ,  G06F9/455 ,  G06N3/063 ,  G06K9/62 ,  G06N3/04 ,  G06V10/44 ,  G06V20/56

Abstract: 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.

公开(公告)号：US09131445B2

公开(公告)日：2015-09-08

申请号：US13894704

申请日：2013-05-15

Applicant: Nvidia Corporation

Inventor： Greg Heinrich ,  Robert Riglar

IPC: H04W4/00 ,  H04W52/02

CPC classification number: H04W52/0229 ,  Y02D70/1262

Abstract: In an aspect there is provided a method of moving a processor of a mobile device from a low-power state for conserving power to an active mode for processing signals. The mobile device is configured to receive regularly scheduled signals. The method comprises, for each of multiple operating states of the mobile device determining a restore time associated with the operating state of the mobile device and storing each determined restore time in association with its operating state. The method further comprises detecting a current operating state of the mobile device and using the determined restore time for that state to set a trigger time to control movement of the processor of the mobile device to enter the active mode from the low-power mode in time to process the scheduled signals.

Abstract translation: 在一方面，提供了一种将移动设备的处理器从低功率状态移动以将功率节省到用于处理信号的活动模式的方法。 移动设备被配置为接收定时调度的信号。 该方法包括针对移动设备的多个操作状态中的每一个确定与移动设备的操作状态相关联的恢复时间，并且与其操作状态相关联地存储每个确定的恢复时间。 该方法还包括检测移动设备的当前操作状态并且使用所确定的恢复时间来设置触发时间，以控制移动设备的处理器从低功率模式中进入活动模式的时间 处理调度信号。

公开(公告)号：US11436484B2

公开(公告)日：2022-09-06

申请号：US16366875

申请日：2019-03-27

Applicant: NVIDIA Corporation

Inventor： 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

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/00 ,  G06F9/455 ,  G06N3/063 ,  G06K9/62 ,  G06V10/44 ,  G06V20/56

Abstract: 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.

公开(公告)号：US08958390B2

公开(公告)日：2015-02-17

申请号：US13629838

申请日：2012-09-28

Applicant: Nvidia Corporation

Inventor： Greg Heinrich ,  Frederic Bossy

IPC: H04W4/00

CPC classification number: G01S19/235 ,  H04J3/0688 ,  H04W56/00

Abstract: An apparatus comprising: a first transceiver arranged to communicate over a wireless network, the first transceiver comprising a first clock; and a second transceiver arranged to communicate other than by said wireless network, the second transceiver comprising a second clock. The second sends a request signal to the first transceiver. In response, the first transceiver transitions from a first mode to a second mode and provides to the second transceiver a response signal for calibrating the second clock relative to the first clock. In the first mode the first transceiver performs zero or more calibrations of the first clock relative to the wireless network, and in the second mode the first transceiver performs at least one additional calibration of the first clock relative to the wireless network, the response signal being based on the at least one additional calibration.

Abstract translation: 一种装置，包括：布置成通过无线网络进行通信的第一收发机，所述第一收发机包括第一时钟; 以及第二收发机，被布置成除了所述无线网络之外进行通信，所述第二收发机包括第二时钟。 第二个发送请求信号给第一收发器。 作为响应，第一收发器从第一模式转换到第二模式，并且向第二收发器提供用于相对于第一时钟校准第二时钟的响应信号。 在第一模式中，第一收发器相对于无线网络对第一时钟执行零次或多次校准，并且在第二模式中，第一收发器相对于无线网络执行第一时钟的至少一次附加校准，响应信号为 基于所述至少一个附加校准。