Samsung Electronics Co., Ltd.

Sangsoo Ko

Sanghyuck Ha

Byeoungsu Kim

Kyoungyoung Kim

Jaegon Kim

Sunggi Yang

Hyoungjun Jeon

Doyun Kim

Sangmin Suh

ByoungJoong Kang

An advanced driver assist system (ADAS) includes a processing circuit and a memory storing instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to: obtain, from a vehicle, a video sequence including a plurality of frames captured while driving the vehicle, where each of the frames corresponds to a stereo image including a first viewpoint image and a second viewpoint image; determine depth information in the stereo image based on reflected signals received while driving the vehicle; fuse the stereo image and the depth information to generated fused information, and detect at least one object included in the stereo image based on the fused information.

Advanced driver assist system, method of calibrating the same, and method of detecting object in the same

An image sensor module includes an image sensor configured to generate image data; a memory including a processor in memory (PIM) circuit, of the PIM circuit including banks configured to store the image data and a plurality of processing elements; and a signal processor, wherein the memory is configured to read the image data from the banks, perform a first image processing operation on the image data by using the processing elements corresponding to the banks, and store image-processed image data on which the first image processing operation is performed in the banks or output the image-processed image data.

Image sensor module and method of operating the same

A method of operating a neural network device including a plurality of layers, includes receiving sensing data from at least one sensor, determining environmental information, based on the received sensing data, determining multiple layers corresponding to the determined environmental information, and dynamically reconstructing the neural network device by changing at least two layers, among the plurality of layers, to the determined multiple layers.

Method and system for learning neural network and device

ADAS includes a processing circuit and a memory which stores instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to receive, from a vehicle that is in motion, a video sequence, generate a position image including at least one object included in the stereo image, generate a second position information associated with the at least one object based on reflected signals received from the vehicle, determine regions each including at least a portion of the at least one object as candidate bounding boxes based on the stereo image and the position image, and selectively adjusting class scores of respective ones of the candidate bounding boxes associated with the at least one object based on whether a respective first position information of the respective ones of the candidate bounding boxes matches the second position information.

Advanced driver assist system and method of detecting object in the same

A streaming accelerator includes a first pool, a first switch bus, a second pool and a second switch bus. The first pool includes neural processing unit (NPU) bundles, and each of NPU bundles includes a plurality of NPUs. The first switch bus provides a first streaming data to a first selected NPU bundle and a second selected NPU bundle respectively. The second pool includes network interface card (NIC) bundles, and each of the NIC bundles includes an encoder and a NIC. The second switch bus provides a first intermediate streaming data and a second intermediate streaming data to a first selected NIC bundle and a second selected NIC bundle. The first selected NIC bundle encodes the first intermediate streaming data to generate a first encoded streaming data. The second selected NIC bundle encodes the second intermediate streaming data to generate a second encoded streaming data.

Streaming accelerators and streaming systems including the same

An electronic device configured to control a host vehicle includes: an image sensor configured to photograph a surrounding environment of the host vehicle; and a processor configured to perform an image processing operation based on a first image captured by the image sensor, and control the host vehicle based on the processing result, wherein the processor determines whether to use a high speed performance of the image processing operation based on a speed of the host vehicle, and the electronic device is configured such that when the high speed performance is not used, the processor performs the image processing operation by using a first image processing module, and when the high speed performance is used, the processor performs the image processing operation by using a second image processing module having less data throughput than the first image processing module.

Electronic device for controlling driving vehicle and operation method of the electronic device

An image sensor module includes an image sensor configured to generate image data and memory including at least a memory bank storing the image data and a processor-in-memory (PIM) circuit, the PIM circuit including a plurality of processing elements. The memory is configured to read the image data from the memory bank; generate optical flow data and pattern density data using the plurality of processing elements, the optical flow data indicating time-sequential motion of at least one object included in the image data, and the pattern density data indicating a density of a pattern of the image data; and output the image data, the optical flow data, and the pattern density data.

Image sensor module, image processing system, and operating method of image sensor module

A neural network system for processing a neural network model including an operation processing graph that includes a plurality of operations, includes an operation processor including an internal memory storing a first module input feature map. The operation processor is configured to obtain a first branch output feature map by performing a first operation among the plurality of operations, based on the stored first module input feature map, and obtain a second branch output feature map by performing a second operation among the plurality of operations after the first operation is performed, based on the stored first module input feature map. The internal memory maintains storage of the first module input feature map while the first operation is performed.

Neural network device, neural network system, and method of processing neural network model by using neural network system

A semiconductor memory device is provided. The semiconductor memory device includes a memory core including a plurality of memory cells configured to store a plurality of data received from an external processor; and a statistical feature extractor disposed on a data path between the external processor and the memory core, the statistical feature extractor being configured to analyze statistical characteristics of the plurality of data, identify at least one statistical feature value associated with the statistical characteristics, store the at least one statistical feature value and transmit the at least one statistical feature value to the external processor.

Semiconductor memory device and electronic system the same

A computing device for training an artificial neural network model includes: a model analyzer configured to receive a first artificial neural network model and split the first artificial neural network model into a plurality of layers; a training logic configured to calculate first sensitivity data varying as the first artificial neural network model is pruned, calculate a target sensitivity corresponding to a target pruning rate based on the first sensitivity data, calculate second sensitivity data varying as each of the plurality of layers is pruned, and output, based on the second sensitivity data, an optimal pruning rate of each of the plurality of layers, the optimal pruning rate corresponding to the target pruning rate; and a model updater configured to prune the first artificial neural network model based on the optimal pruning rate to obtain a second artificial neural network model, and output the second artificial neural network model.

Computing device for training artificial neural network model, method of training the artificial neural network model, and memory system for storing the same

An electronic device includes: a first image sensor that outputs a first image produced by photographing a first viewing angle; a second image sensor that outputs a second image produced by photographing a second viewing angle that overlaps a portion of the first viewing angle; a third image sensor that outputs a third image produced by photographing a third viewing angle; and a processor that performs object detection on an object included in an image. The processor generates disparity information indicating a separation degree of a feature point of the first and second images, transforms the third image based on the disparity information, and performs object detection on the transformed third image.

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Suwon-si, South Korea

Sangsoo Ko