Samsung Electronics Co., Ltd.

Parichay Kapoor

Jin-sung Kim

Hyun-Jung Kim

Dongsoo Lee

Byeoungwook Kim

Pankaj Agarwal

Sejung Kwon

Baeseong Park

Nikunj Saunshi

Mehul Kumar

Hyun-jung Kim

Young-chul Cho

Hyunsik Kim

Won-Sub Kim

Sun-hwa Kim

Yong-sup Park

Jinseok Lee

Ji-hun Oh

Hwakyung Kim

Rakie Kim

Apoorv Kansal

Sunho Moon

James Russell Geraci

Young-Chul Cho

Jin-Hyo Kim

Wonjin Kim

Keongho Lee

Vishnu Vinjam

Hyunghoon Kim

Jisu Oh

Dong-Gi Jang

Seung-Beom Lee

Saehyung Lee

Kyoungsoo Cho

Ji-Hun Oh

Sameer Kumar Agrawal

Subin Jo

Minwook Ahn

Changhun Yu

Kwang-hoon Son

Yong-Sup Park

Kwang-Hoon Son

A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.

Decompression apparatus for decompressing a compressed artificial intelligence model and control method thereof

An electronic apparatus is provided. The electronic apparatus includes sample data and memory storing a first matrix included in an artificial intelligence model trained based on sample data, and a processor configured to prunes each of a plurality of first elements included in the first matrix based on a first threshold, and acquire a first pruning index matrix that indicates whether each of the plurality of first elements has been pruned with binary data, factorize the first matrix to a second matrix of which size was determined based on the number of rows and the rank, and a third matrix of which size was determined based on the rank and the number of columns of the first matrix, prunes each of a plurality of second elements included in the second matrix based on a second threshold, and acquire a second pruning index matrix that indicates whether each of the plurality of second elements has been pruned with binary data, prunes each of a plurality of third elements included in the third matrix based on a third threshold, and acquire a third pruning index matrix that indicates whether each of the plurality of third elements has been pruned with binary data, acquire a final index matrix based on the second pruning index matrix and the third pruning index matrix, and update at least one of the second pruning index matrix or the third pruning index matrix by comparing the final index matrix with the first pruning index matrix.

Electronic apparatus and control method thereof

An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

A method of controlling a notification output from an electronic device according to the present invention may comprise the steps of: outputting notification information; display a snoozing time setting object; using the displayed snoozing time setting object and setting a snoozing time; and executing a snoozing mode for the notification information, and re-outputting the notification information after the selected snoozing time elapses. However, the present invention is not limited to the above embodiment and may include other embodiments.

Electronic device and method for controlling snooze notification

An electronic device is provided. The electronic device includes a memory storing parameter sets, each of which includes one weight and bias sets respectively corresponding to n (where, n&#x3e;1) occlusion levels among a plurality of occlusion levels within a certain range and at least one processor configured to obtain output data by inputting input data to a neural network.

Method for processing input on basis of neural network learning and apparatus therefor

Decompression apparatus and control method thereof

A super-resolution processing method of a moving image is provided. The super-resolution processing method of a moving image includes sequentially inputting a plurality of input frames included in the video to any one of a recurrent neural network (RNN) for super-resolution processing and a convolutional neural network (CNN) for super-resolution processing, sequentially inputting a frame sequentially output from the any one of the RNN and the CNN to an additional one of the RNN and the CNN, and upscaling a resolution of the output frame by carrying out deconvolution with respect to a frame sequentially output from the additional one of the RNN and the CNN.

Super-resolution processing method for moving image and image processing apparatus therefor

A user terminal device includes a touch screen including a main display area and an auxiliary display area including a curved portion extending from the main display area, and a processor configured to, in response to an input for moving an icon displayed on the main display area to the auxiliary display area, control the touch screen to display the icon on the auxiliary display area.

User terminal device and displaying method thereof

The present disclosure relates to system resource management in a variety of situations. The present disclosure provides a method and an apparatus for reducing memory requirements and improving processing speed when an electronic device performs padding for a particular arithmetic operation on data. To achieve the above objective, a method for operating an electronic device according to the present disclosure comprises the steps of: reading a first portion of data from a first memory; determining a first padding address based on the address of a byte belonging to a boundary region of the data among a plurality of bytes included in the first portion; writing values of the plurality of bytes and a value corresponding to the first padding address to a second memory; and reading a second portion of the data from the first memory.

Method and apparatus for system resource management

The present disclosure relates generally to technologies for sensor networks, machine-to-machine (M2M), machine-type communication (MTC), and Internet of things (IoT). The present disclosure may be used in intelligent services (smart home, smart building, smart city, smart car, or connected car, health-care, digital education, retail business, security and safety-related services, etc.), or the like, without limitation. According to the present disclosure, a method for locking a device comprises displaying a screen requesting selection of an image category for a lock screen when an input corresponding to a lock request through a display unit supporting a touch input is detected; displaying a screen requesting setting a password for unlocking when an input corresponding to the selection of the image category is detected; and selecting images supporting the password setting among images included in the selected image category into a group when an input corresponding to the password setting is detected, and locking the touch input to the display unit by displaying one of the selected images.

Method for locking and unlocking touchscreen-equipped mobile device and mobile device

Growing community of inventors

Suwon-si, South Korea

Parichay Kapoor