Samsung Electronics Co., Ltd.

Sangil Jung

Changyong Son

Jinwoo Son

Jaejoon Han

Byung In Yoo

Seohyung Lee

Seungin Park

Jaehyoung Yoo

Changin Choi

Chang Kyu Choi

Youngjun Kwak

Dongwook Lee

Youngsung Kim

Jae-Joon Han

Dohoon Kim

Hana Lee

Yong-Il Lee

Changrok Moon

Dukseo Park

Seokjong Kim

An image sensor includes a substrate including a plurality of photodiodes, a color filter array having a plurality of color filters, and a horizontal insulating layer disposed between the substrate and the color filter array, and a horizontal insulating layer is formed of only a high-K dielectric material, not including silicon, and having a dielectric constant higher than a dielectric constant of silicon oxide, and has a thickness of equal to or greater than 300 angstroms and equal to or less than 1,000.

Image sensor including simplified structure for horizontal insulating layer

An image sensor includes: a motion detection circuit configured to detect a motion in image frames; and a micro control unit (MCU) configured to adjust at least a portion of a target frame among the image frames based on whether the motion is detected, and detect whether a target object is present based on the adjusted portion of the target frame.

Image sensor and operating method of the image sensor

An object classification method and apparatus are disclosed. The object classification method includes receiving an input image, storing first feature data extracted by a first feature extraction layer of a neural network configured to extract features of the input image, receiving second feature data from a second feature extraction layer which is an upper layer of the first feature extraction layer, generating merged feature data by merging the first feature data and the second feature data, and classifying an object in the input image based on the merged feature data.

Method and apparatus with object classification

A neural network processing method and apparatus based on nested bit representation is provided. The processing method includes obtaining first weights for a first layer of a source model of a first layer of a neural network, determining a bit-width for the first layer of the neural network, obtaining second weights for the first layer of the neural network by extracting at least one bit corresponding to the determined bit-width from each of the first weights for the first layer of a source model corresponding to the first layer of the neural network, and processing input data of the first layer of the neural network by executing the first layer of the neural network based on the obtained second weights.

Neural network processing method and apparatus based on nested bit representation

A system includes: an image sensor configured to acquire an image; an image processor configured to generate a quantized image based on the acquired image using a trained quantization filter; and an output interface configured to output the quantized image.

Method and apparatus with quantized image generation

Disclosed is a processor-implemented data processing method in a neural network. A data processing apparatus includes at least one processor, and at least one memory configured to store instructions to be executed by the processor and a neural network, wherein the processor is configured to, based on the instructions, input an input activation map into a current layer included in the neural network, output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a first representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter.

Method and apparatus with neural network parameter quantization

A processor-implemented neural network method includes: obtaining a first weight kernel of a weight model and pruning information of the first weight kernel; determining, based on the pruning information, a processing range of an input feature map for each weight element vector of the first weight kernel; performing a convolution operation between the input feature map and the first weight kernel based on the determined processing range; and generating an output feature map of a neural network layer based on an operation result of the convolution operation.

Method and apparatus with neural network operation processing

Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

Data processing method and sensor device for performing the same

Disclosed is a method and apparatus for face detection using an adaptive threshold. The method includes determining a detection box in an input image, calculating a confidence score indicating whether an object in the detection box corresponds to a face, setting an adaptive threshold based on a size of the detection box, and determining whether the object in the detection box corresponds to a face based on comparing the confidence score to the adaptive threshold.

Method and apparatus for face detection using adaptive threshold

A processor-implemented neural network processing method includes: obtaining a kernel bit-serial block corresponding to first data of a weight kernel of a layer in a neural network; generating a feature map bit-serial block based on second data of one or more input feature maps of the layer; and generating at least a portion of an output feature map by performing a convolution operation of the layer using a bitwise operation between the kernel bit-serial block and the feature map bit-serial block.

Neural network apparatus and method with bitwise operation

A processor-implemented method including implementing a deep neural network (DNN) model using input data, generating, by implementing the DNN model, first output data from the DNN model, changing the DNN model, generating, by implementing the changed DNN model using the input data, second output data of the changed DNN model, and determining result data by combining the first output data and the second output data.

Method and apparatus with deep neural network model fusing

An image sensor can include a photoelectric conversion part of an active region of a substrate and a trench in the substrate. A transfer transistor gate electrode can extend from outside the trench into the trench and terminate in the trench to provide an exposed portion of the trench in the photoelectric conversion part.

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Suwon-si, South Korea

Sangil Jung