Electronic device for predicting amount of power consumption by using artificial intelligence model, and operating method thereof

Abstract

The present disclosure is characterized in that an electronic device is caused to: acquire, at a first time point, first information including information on at least one processor; on the basis of providing the first information to an artificial intelligence model stored in a memory, predict the amount of power consumption of the electronic device to be consumed at a second time point after the first time point; on the basis of identifying that the amount of power consumption exceeds a threshold value related to sudden momentary power loss (SMPL) of the electronic device, acquire, by using the artificial intelligence model, a list including control targets of the electronic device for lowering the amount of power consumption at the second time point to be less than the threshold value; and reduce the power consumption of at least one control target selected on the basis of the list.

Description

Electronic device for predicting power consumption using an artificial intelligence model and method of operation thereof

[0001] The present disclosure relates to an electronic device that predicts power consumption using an artificial intelligence model and a method of operating the same.

[0002] Due to advancements in information and communication technology and semiconductor technology, various functions are being integrated into a single portable electronic device (e.g., a smartphone). For instance, electronic devices can implement not only communication functions but also entertainment functions such as games, multimedia functions such as music and video playback, communication and security functions for mobile banking, camera functions for capturing images and videos, and functions for schedule management and electronic wallets. These electronic devices are becoming smaller to allow users to carry them conveniently, and the various functions provided through these devices are also becoming increasingly sophisticated.

[0003] The electronic device may include at least one processor capable of executing various functions. The electronic device may include a battery that stores power to function as a mobile device. The at least one processor may be operated in various modes based on the state of the battery (e.g., temperature, discharge amount, and / or stored power amount). For example, the at least one processor may control the power consumption of the components of the electronic device in consideration of the power consumption state.

[0004] According to one embodiment, the electronic device may include at least one processor comprising a power management circuit and a processing circuit, and one or more storage media. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to acquire first information including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by the power management circuit, or state information of a plurality of hardware included in the electronic device at a first time point. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to predict the amount of power consumed by the electronic device at a second time point after the first time point, based on providing the first information to an artificial intelligence model stored in the memory. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to obtain a list including control targets of the electronic device for lowering the power consumption at a second time point below the threshold value using the artificial intelligence model, based on determining that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to reduce the power consumption of at least one control target selected based on the list.

[0005] According to one embodiment, a method of operating an electronic device may include an operation of obtaining first information at a first time point, which includes at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit included in the electronic device, or state information of a plurality of hardware included in the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of predicting the power consumption of the electronic device to be consumed at a second time point after the first time point, based on providing the first information to an artificial intelligence model stored in the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of obtaining a list of control targets of the electronic device to lower the power consumption at the second time point below the threshold value using the artificial intelligence model, based on confirming that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of reducing the power consumption of at least one control target selected based on the list.

[0006] According to one embodiment, in a computer-readable non-transient storage medium for storing instructions, when the instructions are executed individually or collectively by at least one processor, the electronic device may be caused to obtain, at a first point in time, first information including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit included in the electronic device, or state information of a plurality of hardware included in the electronic device, and based on providing the first information to an artificial intelligence model stored in the electronic device, predict the power consumption of the electronic device to be consumed at a second point in time after the first point in time, and based on confirming that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device, obtain a list including control targets of the electronic device to lower the power consumption at the second point in time below the threshold value using the artificial intelligence model, and cause the power consumption of at least one control target selected based on the list to be reduced.

[0007] FIG. 1 is a block diagram of an electronic device in a network environment according to various embodiments.

[0008] FIG. 2 is a block diagram showing the schematic configuration of an electronic device according to one embodiment.

[0009] Figure 3 is a graph illustrating sudden momentary power loss (SMPL) occurring based on the current consumption of an electronic device exceeding a threshold value according to a comparative example.

[0010] FIG. 4 is a flowchart illustrating a method for preventing SMPL based on power consumption predicted by an electronic device using an artificial intelligence model according to one embodiment.

[0011] FIG. 5 is a flowchart illustrating a method for an electronic device to predict power consumption using an artificial intelligence model according to one embodiment.

[0012] FIG. 6 is a diagram illustrating the operation of an electronic device using an artificial intelligence model to predict power consumption according to one embodiment.

[0013] FIG. 7 is a diagram illustrating a method for an electronic device to adjust the weights of an artificial intelligence model according to one embodiment.

[0014] FIG. 8 is a graph illustrating the operation of an electronic device using an artificial intelligence model to predict power consumption according to one embodiment.

[0015] FIG. 9 is a flowchart illustrating a method for an electronic device to determine a control target for reducing power consumption using an artificial intelligence model according to one embodiment.

[0016] FIG. 10 is a diagram illustrating a method for determining a control target for reducing power consumption using an artificial intelligence model of an electronic device according to one embodiment.

[0017] FIG. 11 is a drawing of a list including control targets for reducing power consumption obtained by an electronic device using an artificial intelligence model according to one embodiment.

[0018] FIG. 12 is a graph illustrating the operation of reducing power consumption based on power consumption predicted by an artificial intelligence model using an electronic device according to one embodiment.

[0019] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or may communicate with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), 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 antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0020] The processor (120) can control at least one other component (e.g., hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., program (140)), and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., sensor module (176) or communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., central processing unit or application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., graphics processing unit, neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use lower power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0021] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary 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)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence is performed, or through a separate server (e.g., server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0022] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, software (e.g., program (140)) and input data or output data for related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0023] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0024] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0025] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0026] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0027] 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) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0028] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0029] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, 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.

[0030] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0031] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0032] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0033] The power management module (188) can manage the power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0034] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0035] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0036] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) can support a 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., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0037] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally created as part of the antenna module (197).

[0038] According to one embodiment, the antenna module (197) can create a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0039] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0040] According to one embodiment, commands or data may be transmitted or received between an electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or 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 provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0041] The functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0042] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform desired characteristics (or objectives) are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0043] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0044] Meanwhile, terms related to 'identify' in the present disclosure may be replaced with 'detect', 'recognize', 'determine', and / or 'sense'.

[0045] FIG. 2 is a block diagram showing the schematic configuration of an electronic device according to one embodiment. FIG. 3 is a graph to explain sudden momentary power loss (SMPL) occurring based on the current consumption of an electronic device exceeding a threshold value according to a comparative embodiment.

[0046] Referring to FIG. 2, an electronic device (201) (e.g., electronic device (101) of FIG. 1) may include a processor (220) (e.g., processor (120) of FIG. 1), memory (230) (e.g., memory (130) of FIG. 1), a power management circuit (e.g., power management integrated circuit (PMIC)) (240), a battery (250), a sensor module (255) (e.g., sensor module (176) of FIG. 1), and a display module (260) (e.g., display module (160) of FIG. 1). According to one embodiment, the electronic device (201) may omit at least one of the components or additionally include other components (e.g., audio module (155), acoustic module (170)).

[0047] According to one embodiment, the processor (220) can control the overall operation of the electronic device (201). For example, the processor (220) may be implemented identically or similarly to the processor (120) of FIG. 1. According to one embodiment, the processor (220) can control at least one other component (e.g., hardware or software component) of the electronic device (201) connected to the processor (220) by executing software (e.g., program (140) of FIG. 1), and can perform data processing or operations based on instructions. According to one embodiment, the instructions may include instructions composed of machine language that can be processed by the electronic device (201) or the processor (220). For example, the instructions may include instructions corresponding to operation instructions used in the program.

[0048] Meanwhile, although FIG. 2 illustrates that the electronic device (201) includes one processor (220), this is exemplary and the technical concept of the present invention may not be limited thereto. For example, the electronic device (201) may include at least one processor (e.g., CPU, DSP, GPU, and / or NPU). For example, the processor (220) may be implemented as at least one processor.

[0049] According to one embodiment, the memory (230) (e.g., the memory (130) of FIG. 1) may store at least one instruction (or instruction) that causes at least one operation of the electronic device (201). When the at least one instruction is executed collectively or individually by the processor (220), it may cause the electronic device (201) to perform the corresponding operation.

[0050] According to one embodiment, the power management circuit (240) can perform the operation of managing the power of the electronic device (201). For example, the power management circuit (240) can check the power required by the components of the electronic device (201). Additionally, the power management circuit (240) can provide the power requested by the corresponding component to the corresponding component based on the power stored in the battery (250). Additionally, the power management circuit (240) can measure or check the power value (e.g., current value) actually provided to the corresponding component. The power management circuit (240) can measure or check the power value (e.g., current value) provided to each component and provide information about the confirmed power value to the processor (220).

[0051] According to one embodiment, the processor (220) may perform a specified operation to prevent sudden momentary power loss (SMPL) from occurring. For example, when the amount of power (or current value) required by the components (or modules) included in the electronic device exceeds the rated current that can be supplied through the battery (250), the battery voltage output from the battery (250) may drop rapidly. SMPL may manifest as a phenomenon in which the electronic device (201) operates abnormally (e.g., system down (or off) or system reboot) when the battery voltage drops rapidly for the reasons described above.

[0052] Referring to FIG. 3, the first graph (310) may represent a current value required by a component (e.g., a processor) included in the electronic device (201). For example, the horizontal axis of the first graph (310) may represent time (e.g., msec), and the vertical axis of the first graph (310) may represent current (e.g., amperes). For example, when the current value required by the component exceeds a threshold value (e.g., 3A) associated with SMPL, SMPL of the electronic device (201) may occur.

[0053] According to a comparative embodiment, a conventional electronic device can perform various operations to reduce the probability of SMPL occurrence in a section (320) where the current value required by the component exceeds a threshold value (e.g., 3A) associated with SMPL. For example, the conventional electronic device could suggest the operation of a processor (e.g., CPU) or reduce the maximum frequency (or clock frequency) of the processor through a temperature-based hot-plug-out signal. However, since the operations of the conventional electronic device could not prevent SMPL occurrence in advance, SMPL could occur while the electronic device was in use. For example, the conventional electronic device could not actively respond to the occurrence of SMPL, and the power of the electronic device could be turned off or rebooted while the electronic device was in use.

[0054] According to one embodiment, the electronic device (201) can predict the power consumption (or current consumption) of the electronic device in advance using an artificial intelligence model, and control the power consumption of the components included in the electronic device (201) based on the predicted current consumption. According to one embodiment, when predicting the power consumption (or current consumption) of the electronic device in advance using an artificial intelligence model, the electronic device (201) can predict an accurate current consumption by performing an operation of comparing the predicted values ​​with the actually measured power consumption. Through this, the electronic device (201) can prevent the occurrence of SMPL in advance.

[0055] According to one embodiment, the processor (220) may obtain information including at least one of the following: information about the processor (220), information about a processor (or task) executed by the processor (220), voltage (or current) applied to the processor (220) by the power management circuit (240), or status information of a plurality of hardware components (e.g., sensor module (255), display module (260)) included in the electronic device (201), in real time or according to a specified period.

[0056] According to one embodiment, at a first time point, first information can be obtained including at least one of information about the processor (220), information about a process executed by the processor (220), a voltage (or current) applied to the processor (220) by the power management circuit (240), or status information of a plurality of hardware components (e.g., sensor module (255), display module (260)) included in the electronic device (201).

[0057] According to one embodiment, information regarding the processor (220) may include information regarding the type of the processor (220), the operating frequency of the processor (220), or the voltage consumed per operating frequency specified for the processor (220). For example, the type of the processor (220) may include information indicating what kind of processor the corresponding processor is. For example, the operating frequency may represent a current frequency value according to the busyness of the process being executed by the processor (220). Additionally, the voltage consumed per operating frequency may be power information specified for the processor (220) chipset (e.g., the type of processor (220)).

[0058] According to one embodiment, information about a process executed by the processor (220) may include information about an application executed (or currently running) by the processor (220) (e.g., information about the type and state of the application). Additionally, information about the process may include information about the operation state (e.g., run, wait, terminate, or low-power mode) of the corresponding process (e.g., application).

[0059] According to one embodiment, the status information of a plurality of hardware included in the electronic device (201) may include information regarding the status of a processor, the status of a sensor module (255), the status of a display module (260) (e.g., whether it is active or brightness), or the status of a microphone (or speaker).

[0060] According to one embodiment, the processor (220) may obtain user usage pattern information as first information. For example, the usage pattern information may include information on the charging pattern of the electronic device (201) (e.g., information on charging frequency or charging time), the usage time of the electronic device (201), or the usage pattern of the electronic device (201) (e.g., information on which application is mainly used).

[0061] According to one embodiment, the processor (220) may obtain information about the surrounding environment of the electronic device (201) as first information. For example, the information about the surrounding environment may include information about the temperature and / or humidity around the electronic device.

[0062] According to one embodiment, the processor (220) can predict the power consumption (e.g., current value) of the electronic device (201) at a second time point after the first time point based on first information obtained at the first time point using an artificial intelligence model. For example, the processor (220) can obtain information regarding the power consumption (e.g., current value) of the electronic device (201) at the second time point based on providing the first information obtained at the first time point to the artificial intelligence model.

[0063] According to one embodiment, the processor (220) can determine whether the predicted power consumption (e.g., current value) exceeds a threshold related to the sudden momentary power loss (SMPL) of the electronic device (201). For example, the threshold may be a predetermined value (e.g., 3A).

[0064] According to one embodiment, the processor (220) may perform an operation to reduce the power consumption (e.g., current value) at a second time point below the threshold value using an artificial intelligence model when the predicted power consumption (e.g., current value) exceeds the threshold value. For example, the processor (220) may perform an operation to reduce the power consumption of the controlled objects so as to reduce the power consumption of the electronic device (201) at the second time point below the threshold value.

[0065] According to one embodiment, the control targets may include modules implemented in at least one of hardware or software that consume power of the electronic device. For example, the modules implemented in software may include an application executed by the processor (220), a process (or task) executed by the processor, and / or a framework executed by the processor (220). For example, the modules implemented in hardware may include a sensor module (255), a display module (260), an input device of the electronic device (201) (e.g., a microphone), an output device of the electronic device (201) (e.g., a speaker), and / or a plurality of circuits of the electronic device (201).

[0066] According to one embodiment, the processor (220) may obtain a list including control targets of the electronic device (201) to lower the power consumption of the electronic device (201) at a second time point below a threshold value using an artificial intelligence model. The processor (220) may reduce the power consumption of at least one control target selected based on the list. For example, the processor (220) may sequentially terminate control targets having a higher rank among the multiple control targets included in the list. Alternatively, the processor (220) may terminate control targets recommended by the artificial intelligence model among the multiple control targets included in the list.

[0067] According to one embodiment, the artificial intelligence model may include a neural network model trained to predict the power consumption of an electronic device at a second time point after the first time point, based on first information of the electronic device acquired at a first time point. For example, the artificial intelligence model may be stored on a server, and an operation to train the artificial intelligence model on the server may be performed.

[0068] According to one embodiment, the processor (220) can check whether the artificial intelligence model has been updated from the server. If the update of the artificial intelligence model is confirmed, the processor (220) can replace the artificial intelligence model stored in memory (230) with the updated artificial intelligence model. To do this, the processor (220) can obtain the most recently updated artificial intelligence model from the server.

[0069] According to one embodiment, the processor (220) can retrain an artificial intelligence model while predicting the power consumption (e.g., current value) of the electronic device (201). For example, retraining the artificial intelligence model may include the operation of adjusting the weights of the artificial intelligence model.

[0070] According to one embodiment, the processor (220) can obtain a first predicted value based on providing first information to an artificial intelligence model. The processor (220) can compare the first predicted value with a first power consumption measured by a power management circuit (240). Based on comparing the first predicted value with the first power consumption, the processor (220) can adjust the weights applied to the artificial intelligence model to reduce the difference (or error) between the first predicted value and the first power consumption. The processor (220) can obtain a second predicted value based on providing the first information obtained at a third time point to an artificial intelligence model that has been retrained based on the adjusted weights. Based on the second predicted value, the processor (220) can predict the power consumption of the electronic device (201) at a fourth time point after the third time point.

[0071] According to one embodiment, the processor (220) may perform an operation to adjust the weights of an artificial intelligence model a specified number of times (e.g., ticks) within a specified time. For example, the processor (220) may adjust the weights applied to the artificial intelligence model to reduce the difference between the corresponding predicted value and the corresponding actual power consumption based on sequentially comparing a plurality of predicted values ​​and a plurality of power consumptions. The processor (220) may obtain a plurality of predicted values ​​based on providing first information obtained at each time point to an artificial intelligence model that has been retrained based on the adjusted weights. The processor (220) may check the power consumption of the electronic device (201) predicted for the period after the corresponding time point based on the plurality of predicted values.

[0072] By repeatedly performing the above-described operation, the processor (220) can adjust the weights of the artificial intelligence model and, based thereon, predict a power consumption amount that is nearly similar to the actual power measurement. Additionally, the processor (220) can effectively prevent the occurrence of SMPL in the electronic device (201) based on the more accurately predicted power consumption amount.

[0073] At least some of the operations of the electronic device described below may be performed by the processor (220). However, for the convenience of explanation, said operations will be described as being performed by the electronic device (201).

[0074] FIG. 4 is a flowchart illustrating a method for preventing SMPL based on power consumption predicted by an electronic device using an artificial intelligence model according to one embodiment.

[0075] Referring to FIG. 4, according to one embodiment, in operation 401, an electronic device (e.g., the electronic device (201) of FIG. 2) may obtain first information including at least one of information about at least one processor at a first time, information about a task executed by at least one processor, a voltage applied to at least one processor, or state information of a plurality of hardware included in the electronic device. In addition, the electronic device (201) may further obtain at least one of user pattern information or information about the surrounding environment of the electronic device (201) as first information.

[0076] According to one embodiment, in operation 403, the electronic device (201) can predict the amount of power consumed by the electronic device (e.g., current value) at a second time point after a first time point, based on providing first information to an artificial intelligence model.

[0077] According to one embodiment, in operation 405, the electronic device (201) can check whether the predicted power consumption exceeds a threshold related to the SMPL.

[0078] According to one embodiment, if it is confirmed that the predicted power consumption does not exceed a threshold (No in operation 405), the electronic device (201) may not perform a separate operation to lower the power consumption (e.g., current consumption) of the electronic device below the threshold. Additionally, the electronic device (201) may perform an operation to obtain the first information again (e.g., first information obtained at a third time point different from the first time point).

[0079] According to one embodiment, if it is confirmed that the predicted power consumption exceeds a threshold (e.g., operation 405), in operation 407, the electronic device (201) can identify control targets to lower the power consumption (e.g., current consumption) of the electronic device below the threshold using an artificial intelligence model. For example, the electronic device (201) can obtain a list containing control targets using an artificial intelligence model. For example, the artificial intelligence model can output a list containing control targets of the electronic device (201) to lower the power consumption of the electronic device (201) below the threshold. For example, the electronic device (201) can identify control targets to reduce power consumption based on the list. For example, the control targets may include modules implemented as at least one of hardware or software that consumes power of the electronic device.

[0080] According to one embodiment, in operation 409, the electronic device (201) can reduce the power consumption of at least one of the identified control targets. For example, the electronic device (201) can terminate at least one control target recommended by an artificial intelligence model (e.g., a module implemented in at least one of hardware or software).

[0081] Based on the method described above, the electronic device (201) can control the power consumption of the electronic device in advance before SMPL occurs. Through this, the electronic device (201) can effectively prevent SMPL from occurring.

[0082] FIG. 5 is a flowchart illustrating a method for an electronic device to predict power consumption using an artificial intelligence model according to one embodiment.

[0083] Referring to FIG. 5, according to one embodiment, in operation 501, an electronic device (e.g., the electronic device (201) of FIG. 2) can obtain a first predicted value based on providing first information obtained at a first time point to an artificial intelligence model. For example, the electronic device (201) can predict the power consumption of the electronic device (201) at a second time point after the first time point based on the first predicted value.

[0084] According to one embodiment, in operation 503, the electronic device (201) can adjust the weights applied to the artificial intelligence model to reduce the difference between the first predicted value and the first power consumption based on comparing the first predicted value with the actual first power consumption measured by the power management circuit (e.g., the power management circuit (240) of FIG. 4).

[0085] According to one embodiment, in operation 505, the electronic device (201) can obtain a second predicted value based on providing the first information obtained at a third time point to an artificial intelligence model that has been retrained based on adjusted weights.

[0086] According to one embodiment, in operation 507, the electronic device (201) can predict the power consumption of the electronic device (201) at a fourth time point after a third time point based on a second predicted value. Subsequently, the electronic device (201) can continuously predict the power consumption of the electronic device and derive or obtain a predicted value that is nearly similar to the actual power consumption of the electronic device (201).

[0087] According to one embodiment, the electronic device (201) may perform an operation to adjust the weights of the artificial intelligence model a specified number of times (e.g., within 20 times) within a specified time. For example, after operation 507, the electronic device (201) may adjust the weights applied to the artificial intelligence model to reduce the difference between the second predicted value and the second power consumption based on comparing the second predicted value and the second power consumption. Subsequently, the electronic device (201) may adjust the weights applied to the artificial intelligence model to reduce the difference between the third predicted value and the third power consumption based on comparing the third predicted value (e.g., a current value predicted using the weighted artificial intelligence).

[0088] By repeatedly performing the above-described operation (e.g., repeatedly performing it a specified number of times within a specified time), the electronic device (201) can adjust the weights of the artificial intelligence model and, based on this, predict a power consumption amount that is nearly similar to the actual power measurement value.

[0089] FIG. 6 is a diagram illustrating the operation of an electronic device using an artificial intelligence model to predict power consumption according to one embodiment.

[0090] Referring to FIG. 6, according to one embodiment, the preprocessing unit (610), comparison unit (630), and weight determination unit (640) may be implemented as modules (e.g., software) performed by the processor (220).

[0091] According to one embodiment, the preprocessing unit (610) can process the first information into a form suitable for the nodes of the artificial intelligence model (620). For example, the preprocessing unit (610) can process the first information by performing an input node adapting process.

[0092] According to one embodiment, the artificial intelligence model (620) receives first information obtained at a first time point that has been preprocessed as input and can predict the current consumption of the electronic device (201) at a second time point after the first time point. That is, the artificial intelligence model (620) can output the current consumption of the electronic device predicted based on the first information obtained at the first time point. For example, the artificial intelligence model (620) may include a neural network model trained to predict the power consumption (e.g., current consumption) of the electronic device at a second time point after the first time point based on the first information of the electronic device obtained at the first time point.

[0093] According to one embodiment, the comparison unit (630) can compare the current consumption of the electronic device predicted by the artificial intelligence model (620) with the current consumption of the electronic device (201) actually measured by the power management circuit (e.g., power management circuit 240 of FIG. 2). The comparison unit (630) can provide information about the difference between the predicted current consumption and the actual current consumption to the weighting determination unit (640).

[0094] According to one embodiment, the weight determination unit (640) can determine a weight to be applied to the artificial intelligence model (620) based on the difference between the predicted current consumption and the actual current consumption. For example, the weight determination unit (640) can determine a weight to be applied to the artificial intelligence model (620) so that the difference between the predicted current consumption and the actual current consumption becomes smaller. For example, the weight may be a value for adjusting the value of the predicted current consumption output by the artificial intelligence model (620). The weight determination unit (640) can apply the determined weight to the artificial intelligence model (620). For example, the weight determination unit (640) can adjust the weight of the artificial intelligence model (620) to the determined weight based on retraining the artificial intelligence model (620) using the determined weight.

[0095] According to one embodiment, the artificial intelligence model (620) can repeatedly perform the above-described operation while predicting the current consumption of the electronic device. Through this, the artificial intelligence model (620) can be retrained to predict a current consumption that is nearly similar to the actual current consumption of the electronic device (201).

[0096] In FIGS. 7 and 8 below, a method in which an artificial intelligence model (620) repeatedly performs the operation of adjusting weights while predicting the current consumption of an electronic device (201) will be specifically described.

[0097] FIG. 7 is a diagram illustrating a method for an electronic device to adjust the weights of an artificial intelligence model according to one embodiment. FIG. 8 is a graph illustrating an operation for an electronic device to predict power consumption using an artificial intelligence model according to one embodiment.

[0098] Referring to FIG. 7, according to one embodiment, an electronic device (201) can use an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6) to predict the consumption current (h(t)) of the electronic device (201) based on the first information (i(t)) at a specified period (e.g., 640 msec), and can adjust the weights of the artificial intelligence model (620) at the specified period.

[0099] According to one embodiment, an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6) can output the current consumption (h(1)) of an electronic device (201) at a second time point after the first time point, which is predicted based on the first information (i(1)) of the electronic device obtained at the first time point.

[0100] According to one embodiment, the electronic device (201) can compare the actual current consumption (r(1)) of the electronic device (201) measured at a second time point by a power management circuit (e.g., power management circuit (240) of FIG. 2) with the current consumption (h(1)) predicted by an artificial intelligence model (620). The electronic device (201) can determine a first weight (w1) of the artificial intelligence model (620) based on the comparison result. The electronic device (201) can adjust the weight of the artificial intelligence model (620) based on the first weight (w1).

[0101] According to one embodiment, the artificial intelligence model (620) can output the current consumption (h(2)) of the electronic device (201) at a fourth time point after the third time point, which is predicted based on the first information (i(2))) of the electronic device obtained at a third time point (e.g., a time point after the first time point). At this time, the artificial intelligence model (620) may be in a state where the weights are adjusted based on the first weight (w1).

[0102] According to one embodiment, the electronic device (201) can compare the actual current consumption (r(2)) of the electronic device (201) measured at a fourth time point by the power management circuit (240) with the current consumption (h(2)) predicted by the artificial intelligence model (620). The electronic device (201) can determine a second weight (w1) of the artificial intelligence model (620) based on the comparison result. The electronic device (201) can adjust the weight of the artificial intelligence model (620) based on the second weight (w1).

[0103] Based on the above method, the artificial intelligence model (620) can repeatedly perform the operation of adjusting weights while predicting the current consumption of the electronic device (201).

[0104] Referring to FIG. 8, according to one embodiment, the first graph (310) may represent the current value required by a component (e.g., a processor) included in the electronic device (201). The second graph (810) may represent the current consumption of the electronic device (201) predicted by an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6). For example, the horizontal axis of the first graph (310) and the second graph (810) may represent time (e.g., msec), and the vertical axis of the first graph (310) and the second graph (810) may represent current (e.g., amperes).

[0105] According to one embodiment, the weights of the artificial intelligence model (620) may be adjusted so that the difference between the current consumption predicted by the artificial intelligence model (620) and the actual current consumption measured by the power management circuit (240) becomes smaller. For example, the difference between the first graph (310) and the second graph (810) may become smaller over time. For example, in the section (320) where the current value required by the component of the electronic device (201) exceeds a threshold value (e.g., 3A) associated with the SMPL, the second graph (320) may be nearly identical or similar to the first graph (310).

[0106] Through this, the artificial intelligence model (620) can predict a current consumption that is nearly similar to the actual current consumption of the electronic device (201) based on repeatedly adjusting weights while predicting the current consumption of the electronic device (201).

[0107] FIG. 9 is a flowchart illustrating a method for an electronic device to determine a control target for reducing power consumption using an artificial intelligence model according to one embodiment.

[0108] Referring to FIG. 9, according to one embodiment, the electronic device (201) can determine that the current consumed by the electronic device (201) predicted using an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6) exceeds a threshold value associated with SMPL.

[0109] According to one embodiment, in operation 901, an electronic device (e.g., the electronic device (201) of FIG. 2) can check the current consumption of each control target while changing conditions for the control targets of the electronic device (201) using an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6).

[0110] According to one embodiment, in operation 903, the electronic device (201) can obtain a list including control targets based on current consumption using an artificial intelligence model.

[0111] According to one embodiment, in operation 905, the electronic device (201) may terminate control targets having a higher rank among a plurality of control targets included in a list. Alternatively, the electronic device (201) may terminate control targets recommended by an artificial intelligence model among a plurality of control targets included in a list. Alternatively, the electronic device (201) may reduce the processing speed and / or driving frequency for the corresponding control targets without terminating the corresponding control targets.

[0112] According to one embodiment, the electronic device (201) may display a notification requesting the user to terminate the corresponding application in order to control power consumption. Alternatively, the electronic device (201) may display a notification indicating that the corresponding application may be terminated (e.g., forced terminated) soon.

[0113] Through this, the electronic device (201) can control the current consumption of the electronic device (201) so that the current consumption of the electronic device (201) at a predicted time is lower than the threshold value associated with the SMPL.

[0114] FIG. 10 is a diagram illustrating a method for determining a control target for reducing power consumption using an artificial intelligence model of an electronic device according to one embodiment.

[0115] Referring to FIG. 10, according to one embodiment, an electronic device (e.g., the electronic device (201) of FIG. 2) can check the current consumption of each control target while changing conditions for the control targets of the electronic device (201) using an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6).

[0116] According to one embodiment, the electronic device (201) can check the current consumption of the game application (Game 1) while changing the conditions of the game application (Game 1) using an artificial intelligence model (620).

[0117] According to one embodiment, referring to FIG. 10(a), the electronic device (201) can see that when the game application (Game 1) is set to top-activity, the electronic device (201) consumes a first current (e.g., 2.4A). Referring to FIG. 10(b), the electronic device (201) can see that when the game application (Game 1) is not set to top-activity, the electronic device (201) consumes a second current (e.g., 2.37A). Based on changing the conditions for the game application (Game 1), the electronic device (201) can see that the current consumed by the game application (Game 1) corresponds to 0.03A.

[0118] According to one embodiment, the electronic device (201) can check the current consumption of each control target while changing conditions for the control targets according to a method similar to that described above. Based on the current consumption of each control target, the electronic device (201) can determine the control targets that must be terminated so that the current consumption of the electronic device (201) at a predicted time becomes lower than a threshold value associated with the SMPL.

[0119] Meanwhile, the types of applications and current values ​​shown in the table of FIG. 10 are exemplary, and the technical concept of the present invention may not be limited thereto.

[0120] FIG. 11 is a drawing of a list including control targets for reducing power consumption obtained by an electronic device using an artificial intelligence model according to one embodiment.

[0121] Referring to FIG. 11, according to one embodiment, an electronic device (e.g., the electronic device (201) of FIG. 2) can check the current consumption of each control target while changing conditions for the control targets of the electronic device (201) using an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6). Based on the current consumption of each control target, the electronic device (201) can determine the control targets that must be terminated so that the current consumption of the electronic device (201) at a predicted time becomes lower than a threshold value associated with SMPL.

[0122] According to one embodiment, the electronic device (201) may obtain a list (1110) containing a plurality of control targets to be terminated by using an artificial intelligence model (620). The electronic device (201) may also obtain information (1120) indicating a control target recommended for termination among the plurality of control targets included in the list (1110) by using the artificial intelligence model (620). For example, the electronic device (201) may display information (e.g., a list) regarding a plurality of control target applications. For example, the electronic device (201) may display a user interface that allows the user to directly select and terminate an application desired among a plurality of applications. For example, when displaying a plurality of applications, the electronic device (201) may display them in order starting from the application with the highest power consumption. Additionally, the electronic device (201) may display information (e.g., a numerical value) indicating how much power consumption can be improved (or indicating that the electronic device enters a stable state from a dangerous state) when the corresponding application is terminated. That is, the electronic device (201) can display a user interface to provide guidance to the user to exit the desired application.

[0123] According to one embodiment, the electronic device (201) may terminate at least one control target having a high rank among a plurality of control targets included in a list. Alternatively, the electronic device (201) may terminate control targets (modules A, B, and C) recommended by an artificial intelligence model among a plurality of control targets included in a list.

[0124] According to one embodiment, the electronic device (201) may limit the performance of the control targets (modules A, B, and C) or change the operation mode to a low-power mode. In addition, the electronic device (201) may control the control targets using various methods to reduce the power consumption of the electronic device (201).

[0125] Based on the methods described above, the electronic device (201) can terminate at least one control target so that the current consumed by the electronic device (201) at a predicted time becomes lower than a threshold value associated with SMPL.

[0126] FIG. 12 is a graph illustrating the operation of reducing power consumption based on power consumption predicted by an artificial intelligence model using an electronic device according to one embodiment.

[0127] Referring to FIG. 12, according to one embodiment, a graph (1210) may represent a current value required by a component (e.g., a processor) included in an electronic device (201). For example, the horizontal axis of the graph (1210) may represent time (e.g., msec), and the vertical axis of the graph (1210) may represent current (e.g., amperes). For example, when the current value required by the component exceeds a threshold value (e.g., 3A) associated with SMPL, SMPL of the electronic device (201) may occur.

[0128] According to one embodiment, according to one embodiment, the electronic device (201) can predict and confirm that the current consumed by the electronic device (201) in a specific section (320) exceeds a threshold value (e.g., 3A) associated with SMPL using an artificial intelligence model (e.g., the artificial intelligence model (620) of FIG. 6).

[0129] According to one embodiment, the electronic device (201) may terminate at least one control target (e.g., at least one module) of the electronic device (201) in a specific interval (320) so that the current consumed by the electronic device (201) becomes lower than a threshold value associated with the SMPL. By doing so, the current consumed by the electronic device (201) may be reduced in the specific interval (320). For example, the graph (1210) may show a current value lower than the threshold value in the specific interval (320).

[0130] Through the method described above, the electronic device (201) can prevent the occurrence of SMPL in advance and can actively respond to the occurrence of SMPL.

[0131] According to one embodiment, the electronic device may include at least one processor comprising a power management circuit and a processing circuit, and one or more storage media. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to acquire first information including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by the power management circuit, or state information of a plurality of hardware included in the electronic device at a first time point. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to predict the amount of power consumed by the electronic device at a second time point after the first time point, based on providing the first information to an artificial intelligence model stored in the memory. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to obtain a list including control targets of the electronic device for lowering the power consumption at a second time point below the threshold value using the artificial intelligence model, based on determining that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to reduce the power consumption of at least one control target selected based on the list.

[0132] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause to obtain a first predicted value based on providing the first information to the artificial intelligence model. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause to adjust the weights applied to the artificial intelligence model to reduce the difference between the first predicted value and the first power consumption based on comparing the first predicted value with the first power consumption measured by the power management circuit. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause to obtain a second predicted value based on providing the first information obtained at a third time point to the artificial intelligence model to which the adjusted weights are applied. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to predict the power consumption of the electronic device at a fourth time point, which is after a third time point, based on the second predicted value.

[0133] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause the control targets having a higher priority among the plurality of control targets included in the list to terminate first.

[0134] According to one embodiment, the control targets may include modules implemented in at least one of hardware or software that consumes power of the electronic device. According to one embodiment, the modules implemented in software may include an application executed on the electronic device, a process executed on the electronic device, or a framework executed on the electronic device. According to one embodiment, the modules implemented in hardware may include a sensor of the electronic device, an input device of the electronic device, an output device of the electronic device, or a plurality of circuits of the electronic device.

[0135] According to one embodiment, the information regarding the at least one processor may include information regarding the type of the at least one processor, the driving frequency of the at least one processor, or the voltage consumed per driving frequency specified for the at least one processor.

[0136] According to one embodiment, information about the process executed by the at least one processor may include information about the application executed by the at least one processor.

[0137] According to one embodiment, the state information of the plurality of hardware included in the electronic device may include information regarding the state of at least one processor, the state of a sensor included in the electronic device, the state of a speaker included in the electronic device, and the brightness state of a display included in the electronic device.

[0138] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to acquire user usage pattern information as the first information. According to one embodiment, the usage pattern information may include information regarding the charging pattern of the electronic device, the usage time of the electronic device, or the usage pattern of the electronic device.

[0139] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may be caused to acquire information about the surrounding environment of the electronic device as the first information. According to one embodiment, the information about the surrounding environment may include information about the temperature or humidity around the electronic device.

[0140] According to one embodiment, the artificial intelligence model may include a neural network model trained to predict the power consumption based on the first information.

[0141] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause the server to check whether the artificial intelligence model has been updated. According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause the artificial intelligence model to be replaced with the updated artificial intelligence model if the update of the artificial intelligence model is confirmed.

[0142] According to one embodiment, when the instructions are executed collectively or individually by the at least one processor, the electronic device may cause the power consumption of the at least one control target to be controlled in the background of the electronic device.

[0143] According to one embodiment, a method of operating an electronic device may include an operation of obtaining first information at a first time point, which includes at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit included in the electronic device, or state information of a plurality of hardware included in the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of predicting the power consumption of the electronic device to be consumed at a second time point after the first time point, based on providing the first information to an artificial intelligence model stored in the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of obtaining a list of control targets of the electronic device to lower the power consumption at the second time point below the threshold value using the artificial intelligence model, based on confirming that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device. According to one embodiment, a method of operating the electronic device may include an operation of reducing the power consumption of at least one control target selected based on the list.

[0144] The method of operation of the electronic device may further include an operation of obtaining a first predicted value based on providing the first information to the artificial intelligence model. The method of operation of the electronic device may further include an operation of adjusting weights applied to the artificial intelligence model to reduce the difference between the first predicted value and the first power consumption measured by the power management circuit, based on comparing the first predicted value with the first power consumption. The method of operation of the electronic device may further include an operation of obtaining a second predicted value based on providing the first information obtained at a third time point to the artificial intelligence model to which the adjusted weights are applied. The method of operation of the electronic device may further include an operation of predicting the power consumption of the electronic device at a fourth time point, which is after the third time point, based on the second predicted value.

[0145] According to one embodiment, the operation of reducing the power consumption of at least one control target may include the operation of terminating first with the control targets having a higher rank among the plurality of control targets included in the list.

[0146] The method of operating the electronic device may further include the operation of obtaining user usage pattern information as the first information. The usage pattern information may include information regarding the charging pattern of the electronic device, the usage time of the electronic device, or the usage pattern of the electronic device.

[0147] The method of operating the electronic device may further include the operation of obtaining information about the surrounding environment of the electronic device as the first information, and the information about the surrounding environment may include information about the temperature or humidity around the electronic device.

[0148] The method of operation of the electronic device may further include an operation of checking from a server whether the artificial intelligence model has been updated. If an update to the artificial intelligence model is confirmed, the method of operation of the electronic device may further include an operation of replacing the artificial intelligence model with the updated artificial intelligence model.

[0149] According to one embodiment, in a computer-readable non-transient storage medium for storing instructions, when the instructions are executed individually or collectively by at least one processor, the electronic device may be caused to obtain, at a first point in time, first information including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit included in the electronic device, or state information of a plurality of hardware included in the electronic device, and based on providing the first information to an artificial intelligence model stored in the electronic device, predict the power consumption of the electronic device to be consumed at a second point in time after the first point in time, and based on confirming that the power consumption exceeds a threshold value related to the sudden momentary power loss (SMPL) of the electronic device, obtain a list including control targets of the electronic device to lower the power consumption at the second point in time below the threshold value using the artificial intelligence model, and cause the power consumption of at least one control target selected based on the list to be reduced.

[0150] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0151] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as “coupled” or “connected” to another (e.g., 2nd) component, with or without the terms “functionally” or “communicationly,” it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0152] The term “module” as used in the various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0153] Various embodiments of the present document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101)). For example, a processor (e.g., processor (120)) of the machine (e.g., electronic device (101)) may call at least one of the one or more instructions stored in the storage medium and execute it. This enables the machine to be operated to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0154] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0155] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (201), Power management circuit (240); At least one processor (220) including a processing circuit; and The electronic device comprises one or more storage media and includes a memory (230) for storing instructions, and when the instructions are executed collectively or individually by the at least one processor, the electronic device, At a first point in time, first information is obtained including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by the power management circuit, or state information of a plurality of hardware included in the electronic device, and Based on providing the first information to the artificial intelligence model (620) stored in the memory, the amount of power consumed by the electronic device at a second time point after the first time point is predicted, and Based on confirming that the above power consumption exceeds a threshold related to SMPL (sudden momentary power loss) of the electronic device, a list including control targets of the electronic device for lowering the power consumption at the second time point below the threshold using the artificial intelligence model is obtained, and An electronic device that causes a reduction in the power consumption of at least one control target selected based on the above list.

2. In paragraph 1, when the instructions are executed collectively or individually by the at least one processor, the electronic device, A first predicted value is obtained based on providing the above first information to the above artificial intelligence model, and Based on comparing the first predicted value with the first power consumption measured by the power management circuit, the weights applied to the artificial intelligence model are adjusted to reduce the difference between the first predicted value and the first power consumption, and A second predicted value is obtained based on providing the first information obtained at a third time point to the artificial intelligence model to which the above-mentioned adjusted weights are applied, and An electronic device that causes the power consumption of the electronic device at a fourth time point, which is after the third time point, to be predicted based on the second predicted value above.

3. In any one of paragraphs 1 to 2, when the instructions are executed collectively or individually by the at least one processor, the electronic device, An electronic device that causes control targets having a higher priority among a plurality of control targets included in the above list to terminate first.

4. In any one of paragraphs 1 through 3, The above control targets include modules implemented as at least one of hardware or software that consumes power of the electronic device, and Modules implemented by the above software include an application executed on the electronic device, a process executed on the electronic device, or a framework executed on the electronic device, and Modules implemented in the above hardware include a sensor of the electronic device, an input device of the electronic device, an output device of the electronic device, or a plurality of circuits of the electronic device.

5. In any one of paragraphs 1 through 4, The information regarding the at least one processor includes information regarding the type of the at least one processor, the driving frequency of the at least one processor, or the voltage consumption per driving frequency specified for the at least one processor. Information regarding the process executed by the at least one processor includes information regarding the application executed by the at least one processor, and An electronic device comprising, wherein the state information of the plurality of hardware included in the electronic device includes information regarding the state of at least one processor, the state of a sensor included in the electronic device, the state of a speaker included in the electronic device, and the brightness state of a display included in the electronic device.

6. In any one of claims 1 to 5, when the instructions are executed collectively or individually by the at least one processor, the electronic device, Causing to obtain user usage pattern information as the first information, The above usage pattern information includes information regarding the charging pattern of the electronic device, the usage time of the electronic device, or the usage pattern of the electronic device.

7. In any one of claims 1 to 6, when the instructions are executed collectively or individually by the at least one processor, the electronic device, Causing to acquire information about the surrounding environment of the electronic device as the first information, and The information regarding the surrounding environment above includes information regarding the temperature or humidity around the electronic device.

8. In any one of paragraphs 1 through 7, The above artificial intelligence model is an electronic device comprising a neural network model trained to predict the power consumption based on the above first information.

9. In any one of claims 1 through 8, when the instructions are executed collectively or individually by the at least one processor, the electronic device, Check from the server whether the above artificial intelligence model has been updated, and An electronic device that causes the AI ​​model to be replaced with the updated AI model when an update to the AI ​​model is confirmed.

10. In any one of claims 1 to 9, when the instructions are executed collectively or individually by the at least one processor, the electronic device, An electronic device that causes the power consumption of at least one control target to be controlled in the background of the electronic device.

11. In the method of operating the electronic device (201), At a first point in time, an operation of obtaining first information including at least one of information about at least one processor (220) included in the electronic device, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit (240) included in the electronic device, or status information of a plurality of hardware included in the electronic device; An operation of predicting the amount of power consumed by the electronic device at a second time point after the first time point, based on providing the first information to the artificial intelligence model (620) stored in the electronic device; Based on confirming that the above power consumption exceeds a threshold related to SMPL (sudden momentary power loss) of the electronic device, the operation of obtaining a list including control targets of the electronic device for lowering the power consumption at the second time point below the threshold using the artificial intelligence model; and A method of operation of an electronic device comprising an operation to reduce the power consumption of at least one control target selected based on the above list.

12. In Paragraph 11, An operation to obtain a first predicted value based on providing the above first information to the above artificial intelligence model; An operation of adjusting weights applied to the artificial intelligence model to reduce the difference between the first predicted value and the first power consumption measured by the power management circuit, based on comparing the first predicted value and the first power consumption; An operation to obtain a second predicted value based on providing first information obtained at a third time point to the artificial intelligence model to which the above-mentioned adjusted weights are applied; and A method of operating an electronic device further comprising the operation of predicting the power consumption of the electronic device at a fourth time point, which is after a third time point, based on the second predicted value above.

13. In any one of claims 11 to 12, the operation of reducing the power consumption of the at least one control target is, A method of operation of an electronic device comprising terminating first with control targets having a higher rank among a plurality of control targets included in the above list.

14. In any one of paragraphs 11 through 13, The above control targets include modules implemented as at least one of hardware or software that consumes power of the electronic device, and Modules implemented by the above software include an application executed on the electronic device, a process executed on the electronic device, or a framework executed on the electronic device, and A method of operation of an electronic device comprising modules implemented in the above hardware, a sensor of the electronic device, an input device of the electronic device, an output device of the electronic device, or a plurality of circuits of the electronic device.

15. In a computer-readable non-transient storage medium (130, 230) for storing instructions, When the above instructions are executed individually or collectively by at least one processor (220), the electronic device (201) causes, At a first point in time, first information is obtained including at least one of information about the at least one processor, information about a process executed by the at least one processor, a voltage applied to the at least one processor by a power management circuit included in the electronic device, or state information of a plurality of hardware included in the electronic device, and Based on providing the first information to an artificial intelligence model stored in the electronic device, the amount of power consumed by the electronic device at a second time point after the first time point is predicted, and Based on confirming that the above power consumption exceeds a threshold related to SMPL (sudden momentary power loss) of the electronic device, a list including control targets of the electronic device for lowering the power consumption at the second time point below the threshold using the artificial intelligence model is obtained, and A storage medium that causes a reduction in power consumption of at least one control target selected based on the above list.

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