Abstract:
An electronic device is provided. The electronic device includes a touch sensor, a processor, and a memory. The processor may determine a touch input from a user as at least one of a force-touch input or a long-touch input, based on received touch data, determine whether a result of determining the touch data matches an intention of the user, store data that does not match the intention of the user as a result of determination among the touch data in the memory, and determine a type of an artificial intelligence (AI)-based pre-learning model to be used in the electronic device, based on touch input accuracy and the data that does not match the intention of the user.
Abstract:
An electronic device with a cache memory and a method of operating the electronic device are provided. The electronic device includes a cache memory including a plurality of cache lines each of which includes a first area with at least one storage space and a second area with at least one storage space, where the at least one storage space of the first area has a first size and the at least one storage space of the second area has a second size different from the first size, and a cache controller for storing the data requested for storage in one of the storage spaces of the first or second area, according to a compression factor associated with the data requested for storage when a request is made to store data in the cache memory.
Abstract:
Disclosed are a method and apparatus for controlling a cache memory in an electronic device. The apparatus includes a cache memory having cache lines, each of which includes tag information and at least two sub-lines. Each of the at least two sub-lines including a valid bit and a dirty bit. A control unit may analyze a valid bit of a sub-line corresponding to an address tag of data when a request for writing the data is sensed, determine based on activation or deactivation of the valid bit whether a cache hit or a cache miss occurs, and perform a control operation for allocating a sub-line according to a size of the requested data and write the data when the cache hit occurs.
Abstract:
An electronic device and control method for selecting a scaler based on image characteristics are provided. The electronic device includes at least one processor, a display, and a plurality of scalers including a first scaler and a second scaler. The at least one processor generates a first temporary upscaling image of an image by using an algorithm of the first scaler and generate a second temporary up scaling image of the image by using an algorithm of the second scaler. The at least one processor identifies a difference value of a pixel between the first temporary upscaling image and the second temporary upscaling image. The at least one processor selects one scaler based on the identified difference value and a preconfigured threshold value. The at least one processor upscales the image and controls the display to display the upscaled image.
Abstract:
According to certain embodiments, an electronic device comprises: a processor and memory storing instructions; and wherein the instructions, when executed by the processor, further cause the electronic device to: load and compile an artificial intelligence model stored in the memory; determine whether the compiled artificial intelligence model includes a first-type activation function; when the first-type activation function is included in the compiled artificial intelligence model, skip a calculation with respect to a designated value when the designated value exists in a feature map and calculate a value to be calculated subsequent to the designated value; and when the first-type activation function is not included in the compiled artificial intelligence model, perform a calculation with respect to input values of the feature map.
Abstract:
According to various embodiments, an electronic device includes a memory storing deep learning models for determining a force touch, a touchscreen, and a processor configured to identify a touch input of a user through the touchscreen, receive touch pixel data for frames having a time difference based on the touch input, and identify whether the touch input is a force touch based on the touch pixel data. The processor is configured to identify whether the touch input is the force touch using a first determination model among the deep learning models in response to identifying that the touch input is reinputted a designated first number of times or more within a designated time, and otherwise, identify whether the touch input is the force touch using a determination model having a lower computation load than the first determination model among the deep learning models.
Abstract:
According to various embodiments, an electronic device includes a memory storing deep learning models for determining a force touch, a touchscreen, and a processor configured to identify a touch input of a user through the touchscreen, receive touch pixel data for frames having a time difference based on the touch input, and identify whether the touch input is a force touch based on the touch pixel data. The processor is configured to identify whether the touch input is the force touch using a first determination model among the deep learning models in response to identifying that the touch input is reinputted a designated first number of times or more within a designated time, and otherwise, identify whether the touch input is the force touch using a determination model having a lower computation load than the first determination model among the deep learning models.
Abstract:
An electronic device includes a power management circuit; a volatile memory; and a processor configured to: based on the electronic device starting system booting, identify whether the system booting is initial booting or whether a condition designated in the volatile memory is satisfied, based on identifying that the system booting is the initial booting or the condition is satisfied, identify a lower positive supply voltage (LVDD) value for the volatile memory by testing an LVDD margin for the volatile memory, and based on the LVDD value being less than a reference LVDD value for the volatile memory, drive the volatile memory using the LVDD value.
Abstract:
An electronic device is provided. The electronic device includes a memory, and a processor including a resource management unit and a neural processing unit. The processor may be configured to obtain an execution request for a specific function operating based on a specific neural network model, identify an available bandwidth of the memory through the resource management unit, and quantize the specific neural network model based on the available bandwidth of the memory through the neural processing unit.