Ati Technologies Ulc

Advanced Micro Devices Corporation

Alexander S Duenas

Omer Irshad

Mohammad Hamed Mousazadeh

Shahriar Pezeshgi

Jun Huang

Xinzhe Li

Ashwini Chandrashekhara Holla

Oleksandr Khodorkovsky

I-Cheng Chen

Arpit Nitinbhai Patel

Andrew Savio D'Souza

Sishanthy Balachandran

Gabor Sines

Ihab Amer

Michael J Austin

Paul Mackey

Ian Charles Colbert

Joohyun Lee

Mouhanad Alkallas

Muhammad Saad Musani

Hang Zhou

Xuetao Yan

Davis Matthew Castillo

Tsabita Shawnee Rizqa

Stephen Jiacheng Fu

Soon Kyu Kwon

A computing device is provided which comprises memory and a processor in communication with the memory. The processor is configured to autonomously acquire input parameter values, comprising one of monitored device input parameter values from a component of the computing device and monitored user input parameter values. The processor is also configured to select, from a plurality of modes of operation, a mode of operation comprising parameter settings which are determined based on the acquired input parameter values, each of the plurality of modes of operation comprising different parameter settings configured to control the computing device to operate at a different level of performance. The processor is also configured to control operation of the computing device by tuning the parameter settings of the computing device according to the selected mode of operation comprising the determined parameter settings.

System agnostic autonomous system state management

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.

Automated data-driven system to optimize overclocking

An electronic device includes a memory and a processor. The processor acquires a platform management profile, the platform management profile including information defining one or more platform management policies. The processor provides the platform management profile to platform management drivers executing on one or more electronic devices, the platform management profile being configured so that each of the platform management drivers can extract the one or more platform management policies from the platform management profile and use the one or more platform management policies for controlling operating states of elements (e.g., functional blocks, devices, etc.) of the respective electronic device.

Providing platform management profiles to platform management drivers on electronic devices

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.

Application profiling for power-performance management

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.

Adaptive power management

Power management in a computing device. A driver is registered with an operating system (OS) executing on the computing device to receive information about a position of a user interface control. If the user interface control is moved, the driver receives a notification of the user interface control position and determines a power management intervention based on the position The driver transmits the power management intervention to power control circuitry which sets a power setting of the computing device based on the intervention.

Performance and power tuning user interface

An electronic device includes a memory and a processor. The processor receives a platform management profile that includes information defining one or more platform management policies, a given platform management policy among the one or more platform management policies including a provided input from a specified hardware or software sensor and/or a provided output action. The processor uses the given platform management policy for controlling operating states of elements in the electronic device.

Provided inputs and provided output actions for use in platform management policies for platform management drivers

An endpoint processing device is provided for dynamically controlling latency tolerance reporting (LTR) values. The endpoint processing device comprises memory configured to store data and a processor. The processor is configured to execute a program and send, to a root point processing device via a peripheral component interconnect express (PCIe) link, a plurality of messages each comprising a memory access request and a LTR value indicating an amount of time to service the memory access request. The processor is also configured to, for each of the plurality of messages, determine, during execution of the program, a LTR value setting and set the LTR value as the determined LTR value setting.

Dynamic control of latency tolerance reporting values

Power management in a computing device. A driver is registered with an operating system (OS) executing on the computing device to receive information about a position of a user interface control. If the user interface control is moved, the driver receives a notification of the user interface control position and determines a power management intervention based on the position. The driver transmits the power management intervention to power control circuitry which sets a power setting of the computing device based on the intervention.

Various methods and apparatus for graphics processing are disclosed. In one aspect, a method of graphics processing using a computing system is provided. The method includes booting the computing system. After booting the computing system operating video memory of the computing system at a non-overclocked frequency, and prior to rebooting having the computing system sequentially increment the frequency of video memory by a selected change in frequency through a series of overclocked frequencies, after each frequency incrementing writing data to the video memory and testing the stability of the video memory data writing, and if the stability testing fails then decrementing the frequency of the video memory to a previous overclocked frequency at which the stability testing did not fail.

Computing system with automated video memory overclocking

A GPU performs dynamic power level management by switching between pre-defined power levels having distinct clock and voltage levels. The dynamic power level management includes identifying a first performance metric associated with processing workloads at the for a consecutive number of measurement cycles. In some embodiments, the consecutive number of measurement cycles includes a current measurement cycle and at least one previous measurement cycle. Based on a determination that the consecutive number of measurement cycles exceeds a minimum hysteresis number, an estimated optimization is determined to be applied to the GPU for a future measurement cycle. A power level setting at the GPU for the future measurement cycle is adjusted based on the estimated optimization. By considering performance metrics including, for example, different processing workloads and hardware configurations, the GPU is able to dynamically adapt its power settings to the particular workload that it is currently processing.

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Markham, Canada

Alexander S Duenas