Qualcomm Incorporated

Ali Iranli

Steven S Thomson

Bohuslav Rychlik

Sumit Sur

Brian J Salsbery

Norman S Gargash

Christopher Kong Yee Chun

Haw-Jing Lo

Michael Drop

Tao Xue

Vinodh Ramesh Cuppu

Robert A Glenn

Moinul H Khan

Chia-Yuan Teng

Andrew Edmund Turner

George Patsilaras

Min Dai

Moinul H Kahn

Michael J Drop

Aspects include computing devices, systems, and methods for implementing a cache memory access requests for data smaller than a cache line and eliminating overfetching from a main memory by combining the data with padding data of a size of a difference between a size of a cache line and the data. A processor may determine whether the data, uncompressed or compressed, is smaller than a cache line using a size of the data or a compression ratio of the data. The processor may generate the padding data using constant data values or a pattern of data values. The processor may send a write cache memory access request for the combined data to a cache memory controller, which may write the combined data to a cache memory. The cache memory controller may send a write memory access request to a memory controller, which may write the combined data to a memory.

Power aware padding

A method of dynamically controlling power within a multicore CPU is disclosed and may include receiving a degree of parallelism in a workload of a zeroth core and determining whether the degree of parallelism in the workload of the zeroth core is equal to a first wake condition. Further, the method may include determining a time duration for which the first wake condition is met when the degree of parallelism in the workload of the zeroth core is equal to the first wake condition and determining whether the time duration is equal to a first confirm wake condition. The method may also include invoking an operating system to power up a first core when the time duration is equal to the first confirm wake condition.

System and method for controlling central processing unit power based on inferred workload parallelism

Methods, systems and devices that include a dynamic clock and voltage scaling (DCVS) solution configured to compute and enforce performance guarantees for a group of processors to ensure that the processors does not remain in a busy state (e.g., due to transient workloads) for a combined period that is more than a predetermined amount of time above that which is required for one of the processors to complete its pre-computed steady state workload. The DCVS may adjust the frequency and/or voltage of one or more of the processors based on a variable delay to ensure that the multiprocessor system only falls behind its steady state workload by, at most, a predefined maximum amount of work, irrespective of the operating frequency or voltage of the processors.

System and method for controlling central processing unit power with guaranteed transient deadlines

The techniques of the disclosure are directed to reducing power consumption in a device through adaptive backlight level (ABL) scaling. The techniques may utilize a temporal approach in implementing the ABL scaling to adjust the backlight level of a display for a current video frame in a sequence of video frames presented on the display. The techniques may include receiving an initial backlight level adjustment for the current video frame and determining whether to adjust the backlight level adjustment for the current video frame based on a historical trend. The techniques may also determine the historical trend of backlight level adjustments between the current video frame and one or more preceding video frames in the sequence.

Temporal control of illumination scaling in a display device

A method of dynamically controlling power within a central processing unit is disclosed and may include entering an idle state, reviewing a previous busy cycle immediately prior to the idle state, and based on the previous busy cycle determining a CPU frequency for a next busy cycle.

System and method for controlling central processing unit power with reduced frequency oscillations

Methods, systems and devices that include a dynamic clock and voltage scaling (DCVS) solution configured to compute and enforce performance guarantees to ensure that a processor does not remain in a busy state (e.g., due to transient workloads) for more than a predetermined amount of time above that which is required for that processor to complete its pre-computed steady state workload. The DCVS may adjust the frequency and/or voltage of a processor based on a variable delay to ensure that the processing core only falls behind its steady state workload by, at most, a predefined maximum amount of work, irrespective of the operating frequency or voltage of the processor.

Devices and methods for monitoring one or more central processing units in real time are disclosed. The method may include monitoring state data associated with the one or more CPUs in real-time, filtering the state data, and at least partially based on filtered state data, selectively altering one or more system settings. A device may include means for monitoring state data associated with the one or more CPUs in real-time, means for filtering the state data, and means for selectively altering one or more system settings at least partially based on filtered state data. A device may also include a sub-sampling circuit configured to receive a hardware core signal from the central processing unit and output a central processing unit state indication, and an infinite impulse response filter connected to the sub-sampling circuit and configured to receive the central processing unit state indication from the sub-sampling circuit.

System and method of monitoring a central processing unit in real time

A method of controlling power within a multicore central processing unit (CPU) is disclosed. The method may include monitoring a die temperature, determining a degree of parallelism within a workload of the CPU, and powering one or more cores of the CPU up or down based on the degree of parallelism, the die temperature, or a combination thereof.

System and method for dynamically controlling a plurality of cores in a multicore central processing unit based on tempature

A method of controlling power at a central processing unit is disclosed. The method may include moving to a higher CPU frequency after a transient performance deadline has expired, entering an idle state, and resetting the transient performance deadline based on an effective transient budget.

System and method for dynamically controlling a plurality of cores in a multicore central processing unit based on temperature

A method of controlling core clocks in a multicore central processing unit is disclosed and may include executing a zeroth dynamic clock and voltage scaling (DCVS) algorithm on a zeroth core and executing a first DCVS algorithm on a first core. The zeroth DCVS algorithm may operable to independently control a zeroth clock frequency associated with the zeroth core and the first DCVS algorithm may be operable to independently control a first clock frequency associated with the first core.

System and method for asynchronously and independently controlling core clocks in a multicore central processing unit

A method of dynamically controlling power within a multicore central processing unit is disclosed and includes executing a plurality of virtual cores, virtually executing one or more tasks, one or more threads, or a combination thereof at the virtual cores, and physically executing one or more tasks, one or more threads, or a combination thereof at a zeroth physical core. The method may further include receiving a degree of parallelism in a workload of a plurality of virtual cores and determining whether the degree of parallelism in the workload of the virtual cores is equal to a first wake condition.

System and method for controlling central processing unit power in a virtualized system

A method of monitoring one or more central processing units in real time is disclosed. The method may include monitoring state data associated with the one or more CPUs in real-time, filtering the state data, and at least partially based on filtered state data, selectively altering one or more system settings.

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San Diego, CA, United States of America

Ali Iranli