WO2024048943A1 - Dispositif électronique et procédé d'optimisation des performances d'application d'un dispositif électronique - Google Patents

Dispositif électronique et procédé d'optimisation des performances d'application d'un dispositif électronique Download PDF

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Publication number
WO2024048943A1
WO2024048943A1 PCT/KR2023/008908 KR2023008908W WO2024048943A1 WO 2024048943 A1 WO2024048943 A1 WO 2024048943A1 KR 2023008908 W KR2023008908 W KR 2023008908W WO 2024048943 A1 WO2024048943 A1 WO 2024048943A1
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Prior art keywords
application
performance
processor
hls
time
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PCT/KR2023/008908
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English (en)
Korean (ko)
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유종흔
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삼성전자 주식회사
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Priority claimed from KR1020220118891A external-priority patent/KR20240030857A/ko
Application filed by 삼성전자 주식회사 filed Critical 삼성전자 주식회사
Publication of WO2024048943A1 publication Critical patent/WO2024048943A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]

Definitions

  • One embodiment of this document relates to an electronic device and a method of optimizing the performance of an application running on the electronic device.
  • HLS high load segment
  • LLS low load segment
  • a high load state may mean a state in which power consumption to maintain the performance required for application execution exceeds a certain level.
  • a low load state may mean a state in which power consumption to maintain the performance required for application execution is below a certain level.
  • the monitoring program may periodically transmit calculated performance indicators and summarized mobile device status data to the server.
  • An application performance analysis device connected to a server that collects data can analyze multiple performance indicators and summary data transmitted from multiple mobile devices.
  • the application performance analysis device can extract device performance settings for optimal execution of a specific application.
  • the device performance settings delivery server can periodically deliver the above performance settings values to the mobile device.
  • the electronic device can apply the received optimal device performance settings. By performing the above operation periodically, the optimal performance settings of the electronic device required for application execution can be continuously maintained.
  • HLS high load
  • electronic devices can distinguish between high load (HLS) conditions, it can be difficult to predict the duration of HLS on an application-by-application or user-by-user basis. For example, with an application running that includes HLS that lasts for about 30 minutes or more, the electronic device will only last about 20 minutes due to thermal throttling that can be triggered for user safety and device protection. You can run the application with performance settings that are not applicable.
  • Thermal throttling may be a function that reduces heat generation by forcibly lowering the clock and voltage or forcibly turning off the power to protect the electronic device when the electronic device overheats by exceeding a certain temperature. Due to throttling, electronic devices may provide optimal application performance for the first 20 minutes, but may reduce application execution performance for the remaining 10 minutes.
  • the electronic device may include a memory, a communication circuit, and a processor that store performance data of the application according to execution of the application.
  • the processor collects performance data of the application based on the execution of the application, and determines the first time between the time the application is executed and the time a throttling signal for lowering the temperature of the electronic device is generated. and determine a second time, which means the average value of the time the user uses the application, based on the performance data of the application, and a first interface 815 indicating the first time and a second interface indicating the second time. 810 is displayed, and the performance of the application can be adjusted based on user input to the first interface and the second interface.
  • a server may include communication circuitry and a processor.
  • the processor acquires a user database (DB) from a plurality of external devices, extracts data related to application performance from the user database, and divides the extracted performance data into high load segment (HLS) and low load segment (HLS). classified as low load segment (LLS), and based on performance data classified as high load, generate summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage for each of a plurality of external devices, and summarize the summary data.
  • DB user database
  • HLS high load segment
  • HLS low load segment
  • LLS low load segment
  • summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage for each of a plurality of external devices, and summarize the summary data.
  • an HLS period prediction model can be created.
  • the HLS period may refer to a period in which the application operates in a high load state.
  • a method of optimizing the application performance of an electronic device involves collecting performance data of the application based on the execution of the application, from the time the application is executed, to the time when a throttling signal to lower the temperature of the electronic device is generated.
  • the electronic device can predict the duration of the high load (HLS) state on a per-user or per-gaming session basis.
  • HLS high load
  • Electronic devices can maintain peak performance of applications for a predictable duration, providing a high user experience.
  • Electronic devices can calculate the amount of time that throttling will occur for each application while maintaining peak performance.
  • an electronic device can adjust performance for each application.
  • Electronic devices can adjust performance on a per-application basis such that throttling occurs later than the duration of the high load (HLS) state. Accordingly, the electronic device can provide sustainable optimal performance while the user uses the application.
  • HLS high load
  • FIG. 1 is a block diagram of an electronic device in a network environment, according to one embodiment.
  • Figure 2 is a block diagram showing the configuration of an electronic device according to an embodiment.
  • Figure 3 is a block diagram showing the configuration of a server according to an embodiment.
  • FIG. 4 illustrates a method of updating an HLS period prediction model among methods of optimizing the performance of an electronic device according to an embodiment.
  • Figure 5 shows a method of updating an application performance prediction model among the performance optimization methods of an electronic device according to an embodiment.
  • Figure 6 shows a method for optimizing application performance of an electronic device according to an embodiment.
  • FIG. 7 illustrates an operation of evaluating whether the intended performance is achieved after optimizing the application performance of an electronic device according to an embodiment.
  • Figures 8a and 8b show an embodiment of optimizing application performance based on user selection while executing the application.
  • Figure 8c shows an embodiment of optimizing the performance of applications in a situation where multiple applications are executed.
  • Figure 9 is a flowchart showing a method for optimizing application performance of an electronic device according to an embodiment.
  • FIG. 1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments.
  • the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108.
  • a first network 198 e.g., a short-range wireless communication network
  • a second network 199 e.g., a second network 199.
  • the electronic device 101 may communicate with the electronic device 104 through the server 108.
  • the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197.
  • at least one of these components eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101.
  • some of these components e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be.
  • the processor 120 for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134.
  • software e.g., program 140
  • the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132.
  • the commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134.
  • the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor).
  • a main processor 121 e.g., a central processing unit or an application processor
  • auxiliary processor 123 e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.
  • the electronic device 101 includes a main processor 121 and a secondary processor 123
  • the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can.
  • the auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.
  • the auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled.
  • co-processor 123 e.g., image signal processor or communication processor
  • may be implemented as part of another functionally related component e.g., camera module 180 or communication module 190. there is.
  • the auxiliary processor 123 may include a hardware structure specialized for processing artificial intelligence models.
  • Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108).
  • Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited.
  • An artificial intelligence model may include multiple artificial neural network layers.
  • Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above.
  • artificial intelligence models may additionally or alternatively include software structures.
  • the memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto.
  • Memory 130 may include volatile memory 132 or non-volatile memory 134.
  • the program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.
  • the input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user).
  • the input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).
  • the sound output module 155 may output sound signals to the outside of the electronic device 101.
  • the sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback.
  • the receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.
  • the display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user).
  • the display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device.
  • the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.
  • the audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).
  • the electronic device 102 e.g., speaker or headphone
  • the sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do.
  • the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.
  • the interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102).
  • the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.
  • HDMI high definition multimedia interface
  • USB universal serial bus
  • SD card interface Secure Digital Card interface
  • audio interface audio interface
  • connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102).
  • the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).
  • the haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses.
  • the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.
  • the camera module 180 can capture still images and moving images.
  • the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.
  • the power management module 188 can manage power supplied to the electronic device 101.
  • the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).
  • PMIC power management integrated circuit
  • the battery 189 may supply power to at least one component of the electronic device 101.
  • the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.
  • Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication.
  • processor 120 e.g., an application processor
  • the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included.
  • a wireless communication module 192 e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module
  • GNSS global navigation satellite system
  • wired communication module 194 e.g., : LAN (local area network) communication module, or power line communication module
  • the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network
  • the wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199.
  • subscriber information e.g., International Mobile Subscriber Identifier (IMSI)
  • IMSI International Mobile Subscriber Identifier
  • the wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology).
  • NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported.
  • the wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates.
  • the wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna.
  • the wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199).
  • the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC.
  • Peak data rate e.g., 20 Gbps or more
  • loss coverage e.g., 164 dB or less
  • U-plane latency e.g., 164 dB or less
  • the antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device).
  • the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB).
  • the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna.
  • other components eg, radio frequency integrated circuit (RFIC) may be additionally formed as part of the antenna module 197.
  • RFIC radio frequency integrated circuit
  • a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.
  • a first side e.g., bottom side
  • a designated high frequency band e.g., mmWave band
  • a plurality of antennas e.g., array antennas
  • peripheral devices e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)
  • signal e.g. commands or data
  • commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199.
  • Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101.
  • all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108.
  • the electronic device 101 may perform the function or service instead of executing the function or service on its own.
  • one or more external electronic devices may be requested to perform at least part of the function or service.
  • One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101.
  • the electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request.
  • cloud computing distributed computing, mobile edge computing (MEC), or client-server computing technology can be used.
  • the electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing.
  • the external electronic device 104 may include an Internet of Things (IoT) device.
  • Server 108 may be an intelligent server using machine learning and/or neural networks.
  • the external electronic device 104 or server 108 may be included in the second network 199.
  • the electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.
  • Electronic devices may be of various types.
  • Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances.
  • Electronic devices according to embodiments of this document are not limited to the above-described devices.
  • first, second, or first or second may be used simply to distinguish one component from another, and to refer to that component in other respects (e.g., importance or order) is not limited.
  • One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.”
  • any of the components can be connected to the other components directly (e.g. wired), wirelessly, or through a third component.
  • module used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as logic, logic block, component, or circuit, for example. It can be used as A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).
  • ASIC application-specific integrated circuit
  • Various embodiments of the present document are one or more instructions stored in a storage medium (e.g., built-in memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). It may be implemented as software (e.g., program 140) including these.
  • a processor e.g., processor 120
  • the one or more instructions may include code generated by a compiler or code that can be executed by an interpreter.
  • a storage medium that can be read by a device may be provided in the form of a non-transitory storage medium.
  • 'non-transitory' only means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves), and this term refers to cases where data is semi-permanently stored in the storage medium. There is no distinction between temporary storage cases.
  • Computer program products are commodities and can be traded between sellers and buyers.
  • the computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or through an application store (e.g. Play StoreTM) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online.
  • a machine-readable storage medium e.g. compact disc read only memory (CD-ROM)
  • an application store e.g. Play StoreTM
  • two user devices e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online.
  • at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.
  • each component (e.g., module or program) of the above-described components may include a single or plural entity, and some of the plurality of entities may be separately placed in other components. there is.
  • one or more of the components or operations described above may be omitted, or one or more other components or operations may be added.
  • multiple components eg, modules or programs
  • the integrated component may perform one or more functions of each component of the plurality of components in the same or similar manner as those performed by the corresponding component of the plurality of components prior to the integration. .
  • operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, or omitted. Alternatively, one or more other operations may be added.
  • Figure 2 is a block diagram showing the configuration of an electronic device according to various embodiments.
  • the electronic device 200 may include a processor 210 and a communication circuit 220, and some of the illustrated components may be omitted or replaced.
  • the electronic device 200 may include at least some of the configuration and/or functions of the electronic device 101 of FIG. 1 . At least some of the components of the electronic device shown (or not shown) may be operatively, functionally, and/or electrically connected to each other.
  • the processor 210 is a component capable of performing operations or data processing related to control and/or communication of each component of the electronic device 200, and may be composed of one or more processors.
  • the processor 210 may include at least some of the components and/or functions of the processor 120 of FIG. 1 .
  • processor 210 may be performed by loading instructions stored in memory (eg, memory 130 of FIG. 1).
  • the communication circuit 220 may communicate with an external device through a wireless network under the control of the processor 210.
  • the communication circuit 220 is hardware and software for transmitting and receiving data from cellular networks (e.g., long term evolution (LTE) networks, 5G networks, new radio (NR) networks) and local networks (e.g., Wi-Fi, bluetooth).
  • LTE long term evolution
  • NR new radio
  • the communication circuit 220 may include at least some of the components and/or functions of the communication module 190 of FIG. 1 .
  • the HLS period prediction model, throttling period prediction model, and/or application performance prediction model may execute on processor 210.
  • a high load state may mean a state in which power consumption to maintain the performance required for application execution exceeds a certain level.
  • a low load state may mean a state in which power consumption to maintain the performance required for application execution is below a certain level.
  • HLS high load
  • LLS low load
  • the HLS period may refer to a period in which a high load state is maintained when a user uses an application.
  • the length of the HLS period may be determined differently depending on the type of user and application. For example, the length of the HLS period of an application executing a task with a relatively high load may be greater than the length of the HLS period of an application executing a task with a relatively low load.
  • the throttling period prediction model predicts that throttling occurs within the electronic device 200 when running an application with specified performance (e.g., performance of the electronic device 200 while predicting the time required for throttling to occur). You can predict the time it will take. Throttling refers to stopping (or terminating) the execution of an application to protect the electronic device 200 when the temperature of the electronic device 200 exceeds a certain level, or reducing the performance of the application processor (e.g., driving frequency). ) may refer to the action of lowering.
  • An application performance prediction model can determine an application performance level (e.g., running the application processor in a specific frequency band) at which throttling does not occur for a specific period of time.
  • the electronic device 200 may include a communication circuit 220 and a processor 210.
  • the processor 210 collects application performance data (e.g., driving frequency of the application processor) based on the execution of the application, and determines whether the application is a high load segment or a low load segment based on the application performance data. (low load segment), and the first time it takes for a throttling signal to be generated when the supply voltage falls below a preset level when the application maintains a high performance state in response to the application being determined to be a high load.
  • Calculate a second time which means the average value of the time the user uses the application, based on the performance data of the application, and a first interface indicating the first time and a second interface indicating the second time.
  • the processor 210 displays the first interface and the second interface based on determining that the first time is less than the second time, and when the current performance is maintained, throttling may occur during application use, and the application An indicator indicating that performance adjustment is necessary can be displayed.
  • the first time may refer to the time it takes for a throttling signal to occur where the supply voltage falls below a preset level.
  • the second time may mean the average value of the time the user uses the application based on the performance data of the application. If the first time is shorter than the second time, the processor 210 may determine that a throttling signal may occur during application execution.
  • a plurality of parameters indicating the performance of an application may include at least one of screen quality, frame rate, or sound quality.
  • the processor 210 may determine priorities for a plurality of parameters based on user input.
  • the processor 210 may adjust application performance so that the first time exceeds the second time based on the priority determined by the user.
  • the first time may refer to the time it takes for a throttling signal to occur where the supply voltage falls below a preset level.
  • the second time may mean the average value of the time the user uses the application based on the performance data of the application. If the first time exceeds the second time, the processor 210 may control a throttling signal not to be generated while the application is running.
  • the processor 210 may display an indicator indicating that the problem related to throttling has been resolved based on the application performance being adjusted so that the first time exceeds the second time.
  • the processor 210 determines whether a certain number of HLS periods or more have been acquired after acquiring the HLS period prediction model, and determines whether a certain number of HLS periods among the predicted HLS periods are determined based on the number of acquired HLS periods exceeding a preset level. Determine whether the percentage or more matches the actual HLS period, and based on the fact that more than a certain percentage of the predicted HLS periods do not match the actual HLS period, merge the newly acquired HLS period data with the previously obtained HLS period prediction model to perform HLS The period prediction model can be updated.
  • the processor 210 may transmit the updated HLS period prediction model to an external device using the communication circuit 220.
  • the processor 210 determines whether a certain number of application performance data has been obtained after obtaining the application performance prediction model, and based on determining that a certain number of application performance data has been obtained, the predicted application performance level is actually determined. Determine whether it matches the required performance at a certain level or higher, and based on whether the predicted application performance level matches the actual required performance at a certain level or lower, predict the application performance level again based on data from some period during the HLS period, and determine the application performance level. The training of the prediction model can proceed.
  • the processor 210 may transmit an application performance prediction model learned with new data to an external device using the communication circuit 220.
  • the processor 210 may calculate the first time using an application performance prediction model.
  • Figure 3 is a block diagram showing the configuration of a server according to various embodiments.
  • the server 300 includes a processor 310. It may include a communication circuit 220 and a memory 330, and some of the illustrated components may be omitted or replaced. At least some of the components of the server 300 shown (or not shown) may be operatively, functionally, and/or electrically connected to each other.
  • the processor 310 is a component capable of performing operations or data processing related to control and/or communication of each component of the server 300 and may be composed of one or more processors.
  • the processor 310 can implement on the server 300.
  • the processor 310 receives data related to application performance control from the electronic device 200 and updates at least one of an HLS period prediction model, an application performance prediction model, or a throttling period prediction model will be described in detail. Do this. Operations of the processor 310 may be performed by loading instructions stored in the memory 330.
  • the communication circuit 320 may communicate with an external device through a wireless network under the control of the processor 310.
  • the communication circuit 320 is hardware and software for transmitting and receiving data from cellular networks (e.g., long term evolution (LTE) networks, 5G networks, new radio (NR) networks) and local networks (e.g., Wi-Fi, bluetooth).
  • LTE long term evolution
  • NR new radio
  • the communication circuit 320 may include at least some of the configuration and/or functions of the communication module 190 of FIG. 1 .
  • the processor 310 obtains a user database (data base, DB) from a plurality of external devices, extracts data related to the performance of the application from the user database, and applies the extracted performance data to a high load (high load). load segment (HLS) and low load (low load segment, LLS), and generate summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage based on performance data classified as high load, and summarize An HLS period prediction model can be created based on the data.
  • DB data base
  • HLS load segment
  • LLS low load
  • An HLS period prediction model can be created based on the data.
  • the processor 310 may transmit the HLS period prediction model to an external device (eg, a server or a terminal) using the communication circuit 320.
  • an external device eg, a server or a terminal
  • the processor 310 receives application performance prediction models from a plurality of external devices, creates a new application performance prediction model using the plurality of application performance prediction models, and uses a communication circuit to create a new application performance prediction model.
  • the generated application performance prediction model can be transmitted to an external device.
  • Figure 4 shows a method of updating the HLS period prediction model among the performance optimization methods of electronic devices.
  • the operations described with reference to FIG. 4 may be implemented based on instructions that can be stored in a computer recording medium or memory (eg, memory 130 in FIG. 1).
  • the illustrated method 400 may be executed by an electronic device (e.g., the electronic device 200 of FIG. 2) and a server (e.g., the server 300 of FIG. 3) previously described with reference to FIGS. 1 to 3.
  • an electronic device e.g., the electronic device 200 of FIG. 2
  • a server e.g., the server 300 of FIG. 3
  • Technical features that have been described will be omitted below.
  • the order of each operation in FIG. 4 may be changed, some operations may be omitted, and some operations may be performed simultaneously.
  • the processor may determine whether a certain number of HLS periods or more have been acquired after obtaining the HLS period prediction model.
  • the HLS period may refer to a period in which the application operates in a high load state. HLS duration may vary depending on the user.
  • the processor 210 can collect data related to the user's HLS period using a learning model and specify the user's HLS period for each application. If the number of acquired HLS periods is less than a certain level (e.g., about 30), the processor 210 may continue to acquire HLS periods for each application.
  • the processor 210 may perform operation 420 when the number of acquired HLS periods exceeds a certain level (e.g., about 30).
  • the number of HLS periods obtained is only an example and is not limiting.
  • the HLS period prediction model can predict the HLS period of an application based on learned data.
  • the processor 210 may determine whether a certain percentage or more of the predicted HLS periods match actual HLS periods. If the obtained HLS period is included in an area within a certain distance from the center on the normal distribution curve of predicted HLS periods, the processor 210 may determine that the predicted HLS period matches the actual HLS period. If more than a certain percentage (e.g., about 90%) of the predicted HLS periods matches the actual HLS period, the processor 210 may determine in operation 425 that the previously obtained HLS period prediction model has excellent performance.
  • a certain percentage e.g., about 90%
  • the processor 210 adds the newly acquired HLS period data to the previously acquired HLS period prediction model in operation 430. You can update them to a new model by merging them.
  • a certain level e.g., about 90%
  • the processor 210 may transmit the updated HLS period prediction model to the server 300 using the communication circuit 220.
  • the server 300 may periodically receive updated HLS period prediction models from a plurality of terminals, create one HLS period prediction model, and distribute it to multiple terminals.
  • the terminal may include the electronic device 200 of FIG. 2.
  • the server 300 may extract performance data from a user database (DB) collected from a plurality of terminals.
  • the processor 310 of the server 300 may classify the extracted performance data into HLS (high load) and LLS (low load).
  • the processor 310 may generate summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage based on data for a certain period of time (e.g., 1 minute after application use) of each classified HLS.
  • the data measurement period is only an example and is not limited to this.
  • the processor 310 may generate summary data by learning data for a certain period of time and create an HLS period prediction model based on the summary data.
  • the processor 310 may distribute the HLS period prediction model generated to the electronic device 200 using a communication circuit (e.g., the communication circuit 320 of FIG. 3).
  • the electronic device 200 may install or update the distributed HLS period prediction model.
  • Figure 5 shows a method of updating an application performance prediction model among the performance optimization methods of electronic devices.
  • Operations described with reference to FIG. 5 may be implemented based on instructions that can be stored in a computer recording medium or memory (eg, memory 130 in FIG. 1).
  • the illustrated method 500 may be executed by an electronic device (e.g., the electronic device 200 of FIG. 2) and a server (e.g., the server 300 of FIG. 3) previously described with reference to FIGS. 1 to 3.
  • an electronic device e.g., the electronic device 200 of FIG. 2
  • a server e.g., the server 300 of FIG. 3
  • Technical features that have been described will be omitted below.
  • the order of each operation in FIG. 5 may be changed, some operations may be omitted, and some operations may be performed simultaneously.
  • the processor may determine whether a certain number (e.g., about 30) or more application performance data have been acquired after obtaining the application performance prediction model.
  • the number of application performance data obtained is only an example and is not limited to this.
  • the processor 210 may execute operation 520 based on determining that a certain number (e.g., about 30) or more application performance data have been acquired after obtaining the application performance prediction model.
  • An application performance prediction model can determine the application performance level at which throttling does not occur for a certain period of time. Throttling may mean an operation of stopping an application or lowering performance to protect the electronic device 200 when the temperature of the electronic device 200 exceeds a certain level.
  • the processor 210 may determine whether the predicted application performance level matches the actual required performance by a certain level (e.g., about 90%) or more.
  • the ratio between the predicted application performance level and the actual required application performance level is only an example and may vary depending on settings.
  • the processor 210 may determine in operation 525 that the previously obtained application performance prediction model has excellent performance based on the fact that the predicted application performance level matches the actual required performance by a certain level (e.g., about 90%) or more.
  • the processor 210 may perform operation 530 based on the fact that the percentage of the predicted application performance level matching the actual required performance is less than a certain level (e.g., about 90%).
  • the processor 210 may again predict the application performance level and perform learning based on data from HLS for a certain period of time (e.g., 1 minute after using the application).
  • the data measurement period is only an example and is not limited to this.
  • the processor 210 may transmit the application performance prediction model learned with new data to the server 300 using a communication circuit (e.g., the communication circuit 220 of FIG. 2).
  • a communication circuit e.g., the communication circuit 220 of FIG. 2.
  • the server 300 may receive application performance prediction models from a plurality of external devices (eg, terminals) and combine them to create a new prediction model.
  • the server 300 may distribute the generated application performance prediction model to the electronic device 200 using a communication circuit (eg, the communication circuit 320 of FIG. 3).
  • the electronic device 200 may install or update the distributed application performance prediction model.
  • the processor 210 may separate performance data for an application into certain time units. Performance data of an application separated by a certain time unit may be referred to as a session segment.
  • the processor 210 may calculate at least one of FPS, surface temperature, GPU load, and CPU load from the original performance data of session segments separated by a certain time unit. For example, the processor 210 may calculate at least one of the FPS average, minimum value, and maximum value of a single segment, or the difference between the session FPS average and the session segment FPS average for a segment within the same session.
  • the processor 210 may calculate at least one of FPS, surface temperature, GPU load, or CPU load from multiple application data collected from multiple devices.
  • the processor 210 may perform unsupervised learning using the calculated data.
  • An unsupervised learning method can refer to a method of predicting results for new data by clustering data without correct labels into similar features.
  • the processor 210 may label the clusters required for actual performance measurement with HLS based on the clustering result performance index.
  • the processor 210 may perform supervised learning using the values and labeling results used in unsupervised learning.
  • a supervised learning method can refer to a method of learning data using data with the correct answer.
  • the processor 210 may perform supervised learning and create an HLS/LLS classification model.
  • Figure 6 shows a method for optimizing application performance of an electronic device.
  • Operations described with reference to FIG. 6 may be implemented based on instructions that can be stored in a computer recording medium or memory (eg, memory 130 in FIG. 1).
  • the illustrated method 600 may be executed by an electronic device (e.g., the electronic device 200 of FIG. 2) and a server (e.g., the server 300 of FIG. 3) previously described with reference to FIGS. 1 to 3.
  • an electronic device e.g., the electronic device 200 of FIG. 2
  • a server e.g., the server 300 of FIG. 3
  • Technical features that have been described will be omitted below.
  • the order of each operation in FIG. 6 may be changed, some operations may be omitted, and some operations may be performed simultaneously.
  • a processor may detect whether an application is running.
  • the processor 210 may collect performance data for the running application in operation 615 based on the application being executed.
  • Performance data may include at least one of GPU Clock, CPU Clock, temperature, network I/O amount, display brightness, GPU calculation amount, CPU calculation amount, or RAM usage.
  • the processor 210 may calculate at least one of FPS, surface temperature, GPU load, or CPU load from performance data of segments separated by a certain time unit.
  • processor 210 may determine whether the distribution of predicted HLS periods is suitable for adjusting performance.
  • the distribution of predicted HLS periods may be normally distributed.
  • the processor 210 may compare the distribution of the actually measured HLS period and the predicted HLS period to determine in which section the actually measured HLS period is located around the average of the predicted HLS period. If the actual measured HLS period is located within a certain distance around the average of the predicted HLS period, the processor 210 may determine that the distribution of the predicted HLS period is suitable for adjusting performance.
  • the processor 210 may control the electronic device 200 to maintain current performance in operation 625 based on determining that the distribution of the predicted HLS periods is suitable for adjusting performance.
  • processor 210 preprocesses the collected performance data for HLS (high load) and LLS (low load) classification based on determining that the distribution of predicted HLS periods is not suitable for tuning performance. preprocessing) is possible.
  • Data preprocessing may include operations that cleanse the data, handle missing values and outliers, and segment the data. Missing values can refer to data or missing values that cannot be assigned a specific numerical value. Outliers can refer to data that deviates from a trend or data that deviates from the average by a certain level.
  • the processor 210 may create a predictive analysis model using data preprocessing.
  • the processor 210 may perform detection of HLS and LLS through HLS and LLS classification.
  • the processor 210 can determine whether the application is operated in a high load (HLS) state or a low load (LLS) state through detection of HLS and LLS.
  • the processor 210 may collect performance information including any one of GPU, CPU occupancy rate, clock, temperature, and FPS (frame per second) for a certain period of time after executing an application (e.g., a game).
  • the processor 210 may determine whether the application is operated in a high load (HLS) state or a low load (LLS) state based on the collected performance information.
  • the cycle of detection operation of HLS and LLS is not fixed and may vary depending on settings.
  • the processor 210 may check whether the application is operated in a high load (HLS) state or a low load (LLS) state at any given time.
  • the processor 210 may perform performance prediction of an application in the HLS state.
  • the processor 210 may predict a throttling occurrence time based on the predicted application performance and the current state value of the electronic device.
  • the processor 210 may adjust the performance of the electronic device 200 so that the throttling occurrence time exceeds the predicted HLS period.
  • the processor 210 may adjust the performance of the application so that the throttling occurrence time exceeds the predicted HLS period.
  • FIG. 7 illustrates an operation of optimizing the application performance of an electronic device and then evaluating whether the intended performance has been achieved.
  • Operations described through FIG. 7 may be implemented based on instructions that can be stored in a computer recording medium or memory (eg, memory 130 in FIG. 1).
  • the illustrated method 700 may be executed by an electronic device (e.g., the electronic device 200 of FIG. 2) and a server (e.g., the server 300 of FIG. 3) previously described with reference to FIGS. 1 to 3.
  • an electronic device e.g., the electronic device 200 of FIG. 2
  • a server e.g., the server 300 of FIG. 3
  • Technical features that have been described will be omitted below.
  • the order of each operation in FIG. 7 may be changed, some operations may be omitted, and some operations may be performed simultaneously.
  • the processor may generate evaluation data for the HLS period prediction model, throttling period prediction model, and application performance prediction model applied during execution of the application.
  • the HLS period prediction model may refer to a period during which a high load state is maintained when a user uses an application.
  • the HLS period may be determined differently depending on the type of user and application.
  • the throttling period prediction model can predict the time it takes for throttling to occur within the electronic device 200 when running an application with current performance. Throttling may mean an operation of stopping an application or lowering performance to protect the electronic device 200 when the temperature of the electronic device 200 exceeds a certain level.
  • An application performance prediction model can determine the application performance level at which throttling does not occur for a certain period of time.
  • the processor 210 may include an HLS period prediction model, a throttling period prediction model, and an application performance prediction model among its internal components.
  • the processor 210 may update the throttling period prediction model applied during application execution.
  • processor 210 may extract performance data related to the user's application usage. Processor 210 may separate performance data into specific time units. Performance data separated by a certain time unit may be referred to as a segment. Performance data may include at least one of GPU Clock, CPU Clock, temperature, network I/O amount, display brightness, GPU calculation amount, CPU calculation amount, or RAM usage. The processor 210 may calculate at least one of FPS, surface temperature, GPU load, or CPU load from performance data of segments separated by a certain time unit.
  • processor 210 selects the N-3rd through N-1th segments to generate a learning model (e.g., a throttling period prediction model) that predicts the temperature and throttling level of the Nth segment. You can learn.
  • the order of segments is only an example, and the number or order of segments for learning is not limited to this.
  • the processor 210 can use the generated throttling period prediction model to calculate the time for which a specific performance can be maintained and the time to reach a specific temperature when using the application in an idle state.
  • the processor 210 may update the HLS period prediction model applied during application execution. For example, processor 210 may determine whether the actually observed HLS period exceeds an expected level. Processor 210 may determine that the HLS period prediction was poor based on the actually observed HLS period exceeding an expected level. Alternatively, the processor 210 may grant a relatively low HLS period prediction score based on the fact that the actually observed HLS period exceeds the expected level compared to a case where the observed HLS period satisfies the expected level. The processor 210 can obtain average FPS, median FPS, FPS stability, and FPS standard deviation of HLS. The processor 210 may evaluate whether the intended performance was achieved for each parameter based on the obtained parameters. The processor 210 may update the HLS period prediction model based on the evaluation results.
  • the processor 210 may update the application performance prediction model applied during application execution.
  • the processor 210 may transmit to the server 300 the HLS period prediction model, the throttling period prediction model, the application performance prediction model, and performance evaluation data applied during application execution.
  • the server 300 may update at least one of an HLS period prediction model, a throttling period prediction model, and an application performance prediction model using data transmitted from a plurality of terminals.
  • the server 300 may update at least one of an HLS period prediction model, a throttling period prediction model, and an application performance prediction model using performance evaluation data transmitted from a plurality of terminals, and transmit the update to the electronic device 200. .
  • Figures 8a and 8b show an embodiment of optimizing application performance based on user selection while executing the application.
  • a processor may display the predicted HLS period 810 and throttling period 815 on the display.
  • the processor 210 may display related information on the display when the predicted HLS period 810 exceeds the predicted throttling period 815.
  • the processor 210 may display relevant information and display an indicator to the user that if current performance is maintained, throttling may occur while using the application and that application performance needs to be adjusted.
  • processor 210 may detect user input on the interface indicating HLS period 810 and throttling period 815.
  • the processor 210 may display factors that determine the predicted application usage period 820, the time it takes for throttling to occur 825, and application performance based on user input on the interface.
  • Factors that determine application performance may include, for example, screen quality 832, frame rate 834, and sound quality 836.
  • Screen quality 832 may refer to the resolution of the displayed screen.
  • Frame rate 834 may refer to the number of image scenes shown per second.
  • Sound quality 836 may refer to the sound quality level. This is just an example, and the factors that determine application performance are not limited to this and may vary depending on settings.
  • processor 210 determines priorities for screen quality 832, frame rate 834, and sound quality 836 based on user input. You can.
  • processor 210 may adjust application performance based on priorities determined by the user. For example, the processor 210 may increase the time it takes for throttling to occur by limiting only sound-related performance based on the user determining screen quality 832 as the first priority. The processor 210 may display an indicator indicating that the problem related to the occurrence of throttling has been resolved based on the throttling period 815 exceeding the HLS period 810 through performance adjustment.
  • Figure 8c shows an embodiment of optimizing the performance of applications in a situation where multiple applications are executed.
  • the processor 210 may execute the first application 841 and the second application 842 simultaneously.
  • the processor 210 may adjust performance by considering only the HLS period and the throttling period of the first application 841 in the first section 851 in which only the first application 841 is executed.
  • the processor 210 performs the HLS period and the throttling period of the first application 841 and the second application 842 in the second section 853 in which the first application 841 and the second application 842 are executed simultaneously. Performance can be adjusted by taking this into account.
  • the processor 210 may adjust performance by considering only the HLS period and the throttling period of the second application 842 in the third section 855 based on the completion of execution of the first application 841.
  • the processor 210 may adjust the performance of the first application 841 being executed in consideration of the HLS period (T3) of the first application 841.
  • the processor 210 executes the second application 842 in consideration of the HLS period (T3) of the first application (841) and the HLS period (T4) of the second application (842).
  • the performance of the first application 841 and the second application 842 can be adjusted.
  • the processor 210 may adjust the performance of the second application 842 running in consideration of the HLS period (T4) of the second application 842 based on the end of execution of the first application 841. there is.
  • a method of optimizing the application performance of the electronic device 200 includes collecting performance data of the application based on the execution of the application, and optimizing the application performance in a high load segment based on the performance data of the application. ) or low load (low load segment), in response to the application being determined to be a high load, a throttling signal is generated when the supply voltage falls below a preset level when the application maintains a high performance state.
  • Figure 9 is a flowchart showing a method for optimizing application performance of an electronic device according to an embodiment.
  • the operations described with reference to FIG. 9 may be implemented based on instructions that can be stored in a computer recording medium or memory (eg, memory 130 in FIG. 1).
  • the illustrated method 900 may be executed by an electronic device (e.g., the electronic device 200 of FIG. 2) and a server (e.g., the server 300 of FIG. 3) previously described with reference to FIGS. 1 to 3.
  • an electronic device e.g., the electronic device 200 of FIG. 2
  • a server e.g., the server 300 of FIG. 3
  • Technical features that have been described will be omitted below.
  • the order of each operation in FIG. 9 may be changed, some operations may be omitted, and some operations may be performed simultaneously.
  • a processor may collect performance data of an application based on the execution of the application.
  • the processor 210 may transmit the collected application performance data to a server (eg, server 300 in FIG. 3).
  • the processor 210 may obtain or update an application performance prediction model from the server 300.
  • An application performance prediction model can determine the application performance level at which throttling does not occur for a certain period of time. Throttling may mean an operation of stopping an application or lowering performance to protect the electronic device 200 when the temperature of the electronic device 200 exceeds a certain level.
  • the processor 210 may determine a first time period between the time the application is executed and the time a throttling signal for lowering the temperature of the electronic device is generated.
  • the first time may refer to the time it takes for a throttling signal to occur where the supply voltage falls below a preset level.
  • the processor 210 may determine a second time, which is the average value of the time the user uses the application, based on the performance data of the application. If the first time exceeds the second time, the processor 210 may control a throttling signal not to be generated while the application is running.
  • the processor 210 uses a first interface indicating a first time (e.g., the first interface 815 in FIG. 8) and a second interface indicating a second time (e.g., the second interface in FIG. 8). (810)) can be displayed.
  • the processor 210 displays the first interface and the second interface based on determining that the first time is less than the second time, and when the current performance is maintained, throttling may occur during application use, and the application An indicator indicating that performance adjustment is necessary can be displayed.
  • the first time may refer to the time it takes for a throttling signal to occur where the supply voltage falls below a preset level.
  • the second time may mean the average value of the time the user uses the application based on the performance data of the application. If the first time is shorter than the second time, the processor 210 may determine that a throttling signal may occur during application execution.
  • the processor 210 may adjust the performance of the running application based on user input to the first interface and the second interface.
  • the processor 210 may display an indicator indicating that the problem related to throttling has been resolved based on the application performance being adjusted so that the first time exceeds the second time.
  • the processor 210 displays a guide screen including a plurality of parameters indicating the performance of the application based on user input on at least one of the first interface and the second interface, and displays a guide screen containing a plurality of parameters indicating the performance of the application, and displays a guide screen for the user on the guide screen.
  • the performance of the application can be adjusted based on input.
  • a plurality of parameters indicating the performance of an application may include at least one of screen quality, frame rate, or sound quality.
  • the processor 210 may determine priorities for a plurality of parameters based on user input.
  • the electronic device may include a memory, a communication circuit, and a processor that store performance data of the application according to execution of the application.
  • the processor collects performance data of the application based on the execution of the application, and determines the first time between the time the application is executed and the time a throttling signal for lowering the temperature of the electronic device is generated. and determine a second time, which means the average value of the time the user uses the application, based on the performance data of the application, and a first interface 815 indicating the first time and a second interface indicating the second time. 810 is displayed, and the performance of the application can be adjusted based on user input to the first interface and the second interface.
  • the processor 210 displays the first interface 815 and the second interface 810 based on determining that the first time is less than the second time and determines whether throttling during application use while maintaining current performance. This can occur, and an indicator indicating that the application performance needs to be adjusted can be displayed.
  • the processor displays a guide screen including a plurality of parameters indicating the performance of the application based on user input, adjusts the performance of the application based on the user input on the guide screen, and indicates the performance of the application.
  • the plurality of parameters may include at least one of screen quality (832), frame rate (834), or sound quality (836).
  • the processor 210 may determine priorities for a plurality of parameters based on user input.
  • the processor 210 may adjust the performance of the application so that the first time exceeds the second time based on the priority determined by the user.
  • the processor 210 may display an indicator indicating that the problem related to throttling has been resolved based on the performance of the application being adjusted so that the first time exceeds the second time.
  • the processor 210 determines whether more than a certain number of HLS periods have been acquired since the HLS period prediction model was acquired, and determines a certain percentage of the predicted HLS periods based on the number of HLS periods acquired exceeding a preset level. Determine whether the anomaly matches the actual HLS period, and based on the fact that more than a certain percentage of the predicted HLS periods do not match the actual HLS period, merge the newly acquired HLS period data with the previously obtained HLS period prediction model to determine the HLS period The prediction model can be updated.
  • the processor 210 may transmit the updated HLS period prediction model to an external device using the communication circuit 220.
  • the processor 210 determines whether a certain number of application performance data has been obtained after obtaining the application performance prediction model, and determines that the predicted application performance level is actually required based on determining that a certain number of application performance data has been obtained. Determine whether it matches the required performance at a certain level or higher, and based on whether the predicted application performance level matches the actual required performance at a certain level or lower, predict the application performance level again based on data from some period during the HLS period and predict the application performance. You can proceed with model learning.
  • the processor 210 may transmit an application performance prediction model learned with new data to an external device using the communication circuit 220.
  • the processor 210 may calculate the first time using an application performance prediction model.
  • a server may include communication circuitry and a processor.
  • the processor acquires a user database (DB) from a plurality of external devices, extracts data related to application performance from the user database, and divides the extracted performance data into high load segment (HLS) and low load segment (HLS). classified as low load segment (LLS), and based on performance data classified as high load, generate summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage for each of a plurality of external devices, and summarize the summary data.
  • DB user database
  • HLS high load segment
  • HLS low load segment
  • LLS low load segment
  • summary data including at least one of GPU computation amount, CPU computation amount, or RAM usage for each of a plurality of external devices, and summarize the summary data.
  • an HLS period prediction model can be created.
  • the HLS period may refer to a period in which the application operates in a high load state.
  • the processor 310 may transmit the HLS period prediction model to an external device using the communication circuit 320.
  • the processor 310 receives application performance prediction models from a plurality of external devices, generates a new application performance prediction model using the plurality of application performance prediction models, and generates a new application performance prediction model using a communication circuit.
  • the application performance prediction model can be transmitted to an external device.
  • a method of optimizing the application performance of an electronic device involves collecting performance data of the application based on the execution of the application, from the time the application is executed, to the time when a throttling signal to lower the temperature of the electronic device is generated.

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  • Computer Hardware Design (AREA)
  • Debugging And Monitoring (AREA)

Abstract

L'invention concerne un dispositif électronique qui peut comprendre : une mémoire qui stocke des données de performance d'une application selon l'exécution de l'application ; un circuit de communication ; et un processeur. Le processeur peut collecter les données de performance de l'application sur la base de l'exécution de l'application, déterminer un premier moment entre le moment où l'application est exécutée et le moment où un signal d'étranglement pour abaisser la température du dispositif électronique est généré ; déterminer, sur la base des données de performance de l'application, un second moment qui est une valeur moyenne du moment où l'utilisateur utilise l'application, afficher une première interface (815) indiquant le premier moment et une seconde interface (810) indiquant le second moment, et ajuster les performances de l'application sur la base d'une entrée d'utilisateur à la première interface et à la seconde interface.
PCT/KR2023/008908 2022-08-31 2023-06-27 Dispositif électronique et procédé d'optimisation des performances d'application d'un dispositif électronique WO2024048943A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20220109636 2022-08-31
KR10-2022-0109636 2022-08-31
KR10-2022-0118891 2022-09-20
KR1020220118891A KR20240030857A (ko) 2022-08-31 2022-09-20 전자 장치 및 전자 장치의 어플리케이션 성능 최적화 방법

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WO2024048943A1 true WO2024048943A1 (fr) 2024-03-07

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009075855A (ja) * 2007-09-20 2009-04-09 Dainippon Printing Co Ltd リソース使用量取得装置、リソース使用量取得方法、及びリソース使用量取得処理プログラム
KR20180113861A (ko) * 2017-04-07 2018-10-17 삼성전자주식회사 트래픽 제어 방법 및 그 전자 장치
KR20200091670A (ko) * 2019-01-23 2020-07-31 삼성전자주식회사 디스플레이 제어 방법 및 그 전자 장치
KR20210155642A (ko) * 2020-06-16 2021-12-23 삼성전자주식회사 발열 이미지를 이용해 발열을 제어하는 전자 장치 및 그 방법
KR20220060996A (ko) * 2020-11-05 2022-05-12 엑시스 에이비 데이터 저장 장치 온도를 제어하기 위한 방법 및 시스템

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009075855A (ja) * 2007-09-20 2009-04-09 Dainippon Printing Co Ltd リソース使用量取得装置、リソース使用量取得方法、及びリソース使用量取得処理プログラム
KR20180113861A (ko) * 2017-04-07 2018-10-17 삼성전자주식회사 트래픽 제어 방법 및 그 전자 장치
KR20200091670A (ko) * 2019-01-23 2020-07-31 삼성전자주식회사 디스플레이 제어 방법 및 그 전자 장치
KR20210155642A (ko) * 2020-06-16 2021-12-23 삼성전자주식회사 발열 이미지를 이용해 발열을 제어하는 전자 장치 및 그 방법
KR20220060996A (ko) * 2020-11-05 2022-05-12 엑시스 에이비 데이터 저장 장치 온도를 제어하기 위한 방법 및 시스템

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