Abstract: The present disclosure includes cationic carrier units comprising (i) a water-soluble polymer, (ii) a positively charged carrier, (iii) a hydrophobic moiety, and (iv) a crosslinking moiety, wherein when the cationic carrier unit is mixed with an anionic payload (e.g., an antisense oligonucleotide) that electrostatically interacts with the cationic carrier unit, the resulting composition self-organizes into a micelle encapsulating the anionic payload in its core. The cationic carrier units can also comprise a tissue specific targeting moiety, which would be displayed on the surface of the micelle. The disclosure also includes micelles comprising the cationic carrier units of the disclosure, methods of manufacture of cationic carrier units and micelles, pharmaceutical compositions comprising the micelles, and also methods of treating diseases or conditions comprising administering the micelles to a subject in need thereof.

Abstract: The present disclosure includes cationic carrier units comprising (i) a water soluble polymer, (ii) a positively charged carrier, (iii) a hydrophobic moiety, and (iv) a crosslinking moeity, wherein when the cationic carrier unit is mixed with an anionic payload (e.g., an RNA and/or DNA) that electrostatically interacts with the cationic carrier unit, the resulting composition self-organizes into a micelle encapsulating the anionic payload in its core. The cationic carrier units can also comprise a tissue specific targeting moiety, which would be displayed on the surface of the micelle. The disclosure also includes micelles comprising the cationic carrier units of the disclosure, methods of manufacture of cationic carrier units and micelles, pharmaceutical compositions comprising the micelles, and also methods of treating diseases or conditions comprising administering the micelles to a subject in need thereof.

Abstract: An electronic apparatus for performing machine learning a method of machine learning, and a non-transitory computer-readable recording medium are provided. The electronic apparatus includes an operation module configured to include a plurality of processing elements arranged in a predetermined pattern and share data between the plurality of processing elements which are adjacent to each other to perform an operation; and a processor configured to control the operation module to perform a convolution operation by applying a filter to input data, wherein the processor controls the operation module to perform the convolution operation by inputting each of a plurality of elements configuring a two-dimensional filter to the plurality of processing elements in a predetermined order and sequentially applying the plurality of elements to the input data.

Abstract: A sound inspection method of a display device and a sound inspection device including preparing a display device including a display panel and a sound generator disposed on a first surface of the display panel, placing a vibration sensor on a second surface opposite to the first surface of the display panel, vibrating the sound generator at a first reference frequency, vibrating the display panel, and then sensing a vibration of the vibration sensor that vibrates along with a vibration of the display panel; and determining whether a frequency of the vibration of the vibration sensor is included in a first threshold frequency region.

Abstract: Provided are a method and apparatus for processing a convolution operation in a neural network. The apparatus may include a memory, and a processor configured to read, from the memory, one of divided blocks of input data stored in a memory; generate an output block by performing the convolution operation on the one of the divided blocks with a kernel; generate a feature map by using the output block, and write the feature map to the memory.

Abstract: Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

Abstract: Disclosed is an electronic device. The An electronic device including a storage, and a processor configured to perform convolution processing on target data and kernel data based on stride information that indicates an interval at which the kernel data is applied to the target data stored in the storage, in which the processor is further configured to divide the target data into a plurality of pieces of sub-data based on first stride information, perform the convolution processing on the plurality of pieces of sub-data and a plurality of pieces of sub-kernel data respectively corresponding to the plurality of pieces of sub-data based on second stride information that is different from the first stride information, and combine a plurality of processing results, the plurality of pieces of sub-kernel data are obtained by dividing the kernel data based on the first stride information, and the second stride information indicates that the interval at which the kernel data is applied to the target data is 1.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a first memory configured to store a first artificial intelligence (AI) model including a plurality of first elements and a processor configured to include a second memory. The second memory is configured to store a second AI model including a plurality of second elements. The processor is configured to acquire output data from input data based on the second AI model. The first AI model is trained through an AI algorithm. Each of the plurality of second elements includes at least one higher bit of a plurality of bits included in a respective one of the plurality of first elements.

Abstract: Disclosed is an electronic device and method for controlling same. The electronic device comprises: a memory; and a processor which checks an operation instruction for filtering input data of a neural network for each filter of a main pattern selected from a plurality of filters generated according to learning by the neural network, and stores the checked operation instruction in the memory.

Abstract: Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

Abstract: Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

Abstract: A processor for performing deep learning is provided herein. The processor includes a processing element unit including a plurality of processing elements arranged in a matrix form including a first row of processing elements and a second row of processing elements. The processing elements are fed with filter data by a first data input unit which is connected to the first row processing elements. A second data input unit feeds target data to the processing elements. A shifter composed of registers feeds instructions to the processing elements. A controller in the processor controls the processing elements, the first data input unit and second data input unit to process the filter data and target data, thus providing sum of products (convolution) functionality.

Abstract: Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

Abstract: A sound inspection method of a display device and a sound inspection device including preparing a display device including a display panel and a sound generator disposed on a first surface of the display panel, placing a vibration sensor on a second surface opposite to the first surface of the display panel, vibrating the sound generator at a first reference frequency, vibrating the display panel, and then sensing a vibration of the vibration sensor that vibrates along with a vibration of the display panel; and determining whether a frequency of the vibration of the vibration sensor is included in a first threshold frequency region.

Abstract: Disclosed is an electronic device. The An electronic device including a storage, and a processor configured to perform convolution processing on target data and kernel data based on stride information that indicates an interval at which the kernel data is applied to the target data stored in the storage, in which the processor is further configured to divide the target data into a plurality of pieces of sub-data based on first stride information, perform the convolution processing on the plurality of pieces of sub-data and a plurality of pieces of sub-kernel data respectively corresponding to the plurality of pieces of sub-data based on second stride information that is different from the first stride information, and combine a plurality of processing results, the plurality of pieces of sub-kernel data are obtained by dividing the kernel data based on the first stride information, and the second stride information indicates that the interval at which the kernel data is applied to the target data is 1.

Abstract: Disclosed is an electronic device and method for controlling same. The electronic device comprises: a memory; and a processor which checks an operation instruction for filtering input data of a neural network for each filter of a main pattern selected from a plurality of filters generated according to learning by the neural network, and stores the checked operation instruction in the memory.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a first memory configured to store a first artificial intelligence (AI) model including a plurality of first elements and a processor configured to include a second memory. The second memory is configured to store a second AI model including a plurality of second elements. The processor is configured to acquire output data from input data based on the second AI model. The first AI model is trained through an AI algorithm. Each of the plurality of second elements includes at least one higher bit of a plurality of bits included in a respective one of the plurality of first elements.

Abstract: Provided are a method and apparatus for processing a convolution operation in a neural network. The apparatus may include a memory, and a processor configured to read, from the memory, one of divided blocks of input data stored in a memory; generate an output block by performing the convolution operation on the one of the divided blocks with a kernel; generate a feature map by using the output block, and write the feature map to the memory.

Abstract: A processor for performing deep learning is provided herein. The processor includes a processing element unit including a plurality of processing elements arranged in a matrix form including a first row of processing elements and a second row of processing elements. The processing elements are fed with filter data by a first data input unit which is connected to the first row processing elements. A second data input unit feeds target data to the processing elements. A shifter composed of registers feeds instructions to the processing elements. A controller in the processor controls the processing elements, the first data input unit and second data input unit to process the filter data and target data, thus providing sum of products (convolution) functionality.

Abstract: An electronic apparatus and method thereof are provided for performing deep learning. The electronic apparatus includes a storage configured to store target data and kernel data; and a processor including a plurality of processing elements that are arranged in a matrix shape. The processor is configured to input, to each of the plurality of processing elements, a first non-zero element from among a plurality of first elements included in the target data, and sequentially input, to each of a plurality of first processing elements included in a first row from among the plurality of processing elements, a second non-zero element from among the plurality of elements included in the kernel data. Each of the plurality of first processing elements is configured to perform an operation between the input first non-zero element and the input second non-zero element, based on depth information of the first non-zero element and depth information of the second non-zero element.