Dynamic energy performance preference based on workloads using an adaptive algorithm

Patent metadata

Field	Value
Publication number	US-2022187893-A1
Application number	US-202017442374-A
Country	US
Kind code	A1
Filing date	Jul 14, 2020
Priority date	Jul 15, 2019
Publication date	Jun 16, 2022
Grant date	—

Abstract

Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning Technology) and predict device workloads using hardware (HW) counters. These mechanisms and methods may accordingly increase performance and user responsiveness by dynamically changing an Energy Performance Preference (EPP) based on a longer time workload analysis and workload prediction.

First claim

1 - 26 . (canceled)

27 . An apparatus comprising:
one or more hardware components; and a firmware to execute on at least one of the one or more hardware components, wherein the firmware adaptively adjusts an energy performance preference for the one or more hardware components based on parameters including predicted workload and usage behavior of applications executed on the one or more hardware components.

28 . The apparatus of claim 27 , further comprising a machine-learning engine to predict a workload type based on telematic data from the one or more hardware components.

29 . The apparatus of claim 28 , wherein the workload type is one of: idle, semi-active, bursty, sustained, and battery life.

30 . The apparatus of claim 28 , wherein the machine-learning engine has a pre-trained model to predict the workload.

31 . The apparatus of claim 28 , wherein the machine-learning engine is implemented in hardware and/or software.

32 . The apparatus of claim 27 , wherein the energy performance preference is visible to an operating system.

33 . The apparatus of claim 27 , wherein the firmware adjusts frequency and/or voltage of the one or more hardware components according to the adaptively adjusted energy performance preference.

34 . The apparatus of claim 27 , wherein as the energy performance preference is adjusted to a lower value, performance increases for the one or more components, or wherein as the energy performance preference is adjusted to a higher value, energy reduces for the one or more components.

35 . The apparatus of claim 27 , wherein the one or more components include: one or more processor cores, a graphics processing unit, and a mesh or ring fabric.

36 . A machine-readable storage media having machine-executable instructions, that when executed, cause one or more machines to perform operations comprising:
receiving telematic data and performance data from one or more hardware components; providing the telematic data and performance data to a machine-learning engine to predict a workload type; receiving the predicted workload type; adaptively modifying an energy performance preference based on the predicted workload type; and providing the modified energy performance preference to a firmware which in turn adjusts frequency and/or voltage of the one or more components.

37 . The machine-readable storage media of claim 36 , wherein the workload type is one of: idle, semi-active, bursty, sustained, and battery life.

38 . The machine-readable storage media of claim 36 , the energy performance preference is visible to an operating system.

39 . The machine-readable storage media of claim 36 , wherein the energy performance preference is adjusted to a lower value, performance increases for the one or more components, or wherein as the energy performance preference is adjusted to a higher value, energy reduces for the one or more components.

40 . The machine-readable storage media of claim 36 , wherein the machine-learning engine is implemented in software and/or hardware.

41 . The machine-readable storage media of claim 36 , wherein the one or more components include: one or more processor cores, a graphics processing unit, and a mesh or ring fabric.

42 . A system comprising:
a memory; a processor coupled to the memory; a wireless interface communicatively coupled to the processor, wherein the processor includes a power control unit which executes a firmware that adaptively adjusts an energy performance preference for one or more hardware components of the system, including the processor, based on parameters including predicted workload and usage behavior of applications executed on the one or more hardware components; and a machine-learning engine communicatively coupled to the firmware, wherein the machine-learning engine predicts a workload type based on telematic data from the one or more hardware components.

43 . The system of claim 42 , wherein the workload type is one of: idle, semi-active, bursty, sustained, and battery life.

44 . The system of claim 42 , wherein the machine-learning engine has a pre-trained model to predict the workload.

45 . The system of claim 44 , wherein the firmware adjusts frequency and/or voltage of the one or more hardware components according to the adaptively adjusted energy performance preference.

46 . The system of claim 42 , wherein as the energy performance preference is adjusted to a lower value, performance increases for the one or more components or wherein as the energy performance preference is adjusted to a higher value, performance increases for the one or more components.

47 . A method comprising:
receiving telematic data and performance data from one or more hardware components; providing the telematic data and performance data to a machine-learning engine to predict a workload type; receiving the predicted workload type; adaptively modifying an energy performance preference based on the predicted workload type; and providing the modified energy performance preference to a firmware which in turn adjusts frequency and/or voltage of the one or more components.

48 . The method of claim 47 , wherein the workload type is one of: idle, semi-active, bursty, sustained, and battery life.

49 . The method of claim 47 , wherein the energy performance preference is visible to an operating system.

50 . The method of claim 47 , wherein the energy performance preference is adjusted to a lower value, performance increases for the one or more components, or wherein as the energy performance preference is adjusted to a higher value, energy reduces for the one or more components.

51 . The method of claim 47 , wherein the one or more components include: one or more processor cores, a graphics processing unit, and a mesh or ring fabric.

Assignees

• Intel Corp

Inventors

• Sakarda Premanand
• Rotem Efraim
• Weissmann Eliezer
• Abu Salah Hisham
• Beja Hadas
• Fenger Russell
• Ganapathy Deepak
• Hermerding Ii James
• Karavany Ido
• Krishnakumar Nivedha
• Nair Sudheer
• Olswang Gilad
• Peri Moran
• Wagner Avishai
• Wang Zhongsheng
• Yassin Noha

Classifications

• G06F1/3203

  Power management, i.e. event-based initiation of a power-saving mode · CPC title

• G06F1/3296

  by lowering the supply or operating voltage · CPC title

• G06F1/3206 Primary

  Monitoring of events, devices or parameters that trigger a change in power modality · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F1/3212

  Monitoring battery levels, e.g. power saving mode being initiated when battery voltage goes below a certain level · CPC title