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.

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.

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.

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.

Abstract: One embodiment of a method includes adjusting a plurality of hyperparameters corresponding to a plurality of neural networks trained asynchronously relative to each other using a plurality of computer systems. The method further includes asynchronously measuring one or more performance metrics associated with the plurality of neural networks being trained. The method further includes ceasing the adjusting of the plurality of hyperparameters corresponding to one or more of the plurality of neural networks if the one or more performance metrics associated with the one or more of the plurality of neural networks are below a threshold.

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.

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.

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: 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: 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: One aspect provides a modem for use at a terminal. The modem comprises a first interface, a second interface, and a processing unit. The first interface is arranged to connect to a network. The second interface is arranged to connect to a host processor on the terminal. The processing unit is arranged to perform a procedure to attempt to connect to the network via the first interface. The processing unit is also arranged to receive an indication of an operating mode that the host processor is operating in from the host processor via the second interface, where the operating mode is one of a plurality of operating modes. The processing unit is further arranged to, in the event of failure of the procedure, repeat performing the procedure at a time controlled in dependence on the received indication.

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: 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: 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.