Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.

Abstract: A processing device includes an automated overclocking system and a processor. The automated overclocking system is data-driven and includes an inference engine that executes a machine learning model configured to generate a first output based on a current configuration of the processing device. The first output includes a first set of overclocking parameters. The processor is configured to adjust one or more operating characteristics of at least one component of the processing device based on the first set of overclocking parameters.

Abstract: Techniques are described for adaptive device power management. The device interface application of a hardware computing unit detects a launch of an application by the operating system (OS) to be executed on the hardware computing unit, in an implementation. The device interface application identifies the launched application and determines whether a hardware profile exists that is associated with the application. The hardware profile includes one or more hardware parameters that yield the optimal performance for power consumption by the hardware computing unit when executing the launched application. Based on determining that the hardware profile exists, the power policy of the OS is updated for the launched application, and thereby, the driver updates the power state(s) of the hardware computing unit based on the new power policy.

Abstract: The disclosed computer-implemented method for generating remedy recommendations for power and performance issues within semiconductor software and hardware. For example, the disclosed systems and methods can apply a rule-based model to telemetry data to generate rule-based root-cause outputs as well as telemetry-based unknown outputs. The disclosed systems and methods can further apply a root-cause machine learning model to the telemetry-based unknown outputs to analyze deep and complex failure patterns with the telemetry-based unknown outputs to ultimately generate one or more root-cause remedy recommendations that are specific to the identified failure and the client computing device that is experiencing that failure.

Abstract: Techniques are described for adaptive device power management. The device interface application of a hardware computing unit detects a launch of an application by the operating system (OS) to be executed on the hardware computing unit, in an implementation. The device interface application identifies the launched application and determines whether a hardware profile exists that is associated with the application. The hardware profile includes one or more hardware parameters that yield the optimal performance for power consumption by the hardware computing unit when executing the launched application. Based on determining that the hardware profile exists, the power policy of the OS is updated for the launched application, and thereby, the driver updates the power state(s) of the hardware computing unit based on the new power policy.

Abstract: The disclosed computer-implemented method for generating remedy recommendations for power and performance issues within semiconductor software and hardware. For example, the disclosed systems and methods can apply a rule-based model to telemetry data to generate rule-based root-cause outputs as well as telemetry-based unknown outputs. The disclosed systems and methods can further apply a root-cause machine learning model to the telemetry-based unknown outputs to analyze deep and complex failure patterns with the telemetry-based unknown outputs to ultimately generate one or more root-cause remedy recommendations that are specific to the identified failure and the client computing device that is experiencing that failure.

Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.

Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.

Abstract: Techniques are described for adaptive device power management. The device interface application of a hardware computing unit detects a launch of an application by the operating system (OS) to be executed on the hardware computing unit, in an implementation. The device interface application identifies the launched application and determines whether a hardware profile exists that is associated with the application. The hardware profile includes one or more hardware parameters that yield the optimal performance for power consumption by the hardware computing unit when executing the launched application. Based on determining that the hardware profile exists, the power policy of the OS is updated for the launched application, and thereby, the driver updates the power state(s) of the hardware computing unit based on the new power policy.

Abstract: A method, processing system and processor-readable medium for classifying human behavior based on a sequence of frames of a digital video. A 2D convolutional neural network is used to identify key points on a human body, such as human body joints, visible within each frame. An encoded representation of the key points is created for each video frame. The sequence of encoded representations corresponding to the sequence of frames is processed by a 3D CNN trained to identify human behaviors based on key point positions varying over time.

Abstract: A method, processing system and processor-readable medium for classifying human behavior based on a sequence of frames of a digital video. A 2D convolutional neural network is used to identify key points on a human body, such as human body joints, visible within each frame. An encoded representation of the key points is created for each video frame. The sequence of encoded representations corresponding to the sequence of frames is processed by a 3D CNN trained to identify human behaviors based on key point positions varying over time.

Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.

Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.

Abstract: A processing apparatus is provided which includes memory configured to store hardware parameter settings for each of a plurality of applications. The processing apparatus also includes a processor in communication with the memory configured to store, in the memory, the hardware parameter settings, identify one of the plurality of applications as a currently executing application and control an operation of hardware by tuning a plurality of hardware parameters according to the stored hardware parameter settings for the identified application.