Abstract: The present invention relates to a substrate processing apparatus, and more particularly, the substrate processing apparatus includes a gas utility exhausting each of the reaction space and the protective space so that a pressure change process including a high-pressure process, which is in a state of a pressure higher than atmospheric pressure, and a low-pressure process that is in a state of a pressure lower than the atmospheric pressure, is performed on a plurality of substrates introduced into the reaction space.

Abstract: Disclosed is a neural network computing device. The neural network computing device includes a neural network accelerator including an analog MAC, a controller controlling the neural network accelerator in one of a first mode and a second mode, and a calibrator that calibrating a gain and a DC offset of the analog MAC. The calibrator includes a memory storing weight data, calibration weight data, and calibration input data, a gain and offset calculator reading the calibration weight data and the calibration input data from the memory, inputting the calibration weight data and the calibration input data to the analog MAC, receiving calibration output data from the analog MAC, and calculating the gain and the DC offset of the analog MAC, and an on-device quantizer reading the weight data, receiving the gain and the DC offset, generating quantized weight data, based on the gain and the DC offset.

Abstract: A method for controlling a memory from which data is transferred to a neural network processor and an apparatus thereof are provided, the method including: generating prefetch information of data by using a blob descriptor and a reference prediction table after history information is input; reading the data in the memory based on the pre-fetch information and temporarily archiving read data in a prefetch buffer; and accessing next data in the memory based on the prefetch information and temporarily archiving the next data in the prefetch buffer after the data is transferred to the neural network from the prefetch buffer.

Abstract: An air ventilation apparatus for a vehicle using a Coanda effect according to one embodiment of the present invention includes a duct housing having a hollow shape in which an air inlet is formed in one end portion thereof and an air outlet is formed in the other end portion thereof, a cover member having one end portion coupled to surround the other end portion of the duct housing and having the other end portion which is coupled to the board and in which a communication hole is formed to communicate with the discharge port, and an air guide member which is disposed inside the cover member, is hinge-coupled to the duct housing so as to be vertically rotatable, and has a through-hole through which the air outlet communicates with the communication hole.

Abstract: Provided is a convolutional neural network system including a data selector configured to output an input value corresponding to a position of a sparse weight from among input values of input data on a basis of a sparse index indicating the position of a nonzero value in a sparse weight kernel, and a multiply-accumulate (MAC) computator configured to perform a convolution computation on the input value output from the data selector by using the sparse weight kernel.

Abstract: In the present invention, a wind direction is not directly changed using a wind direction adjusting wing and a guide related thereto, but the wind direction is changed due to the Coanda effect and the vortex formation. A basic principle of the present invention of adjusting a wind direction by applying a Coanda effect is to design the air ventilation apparatus such that a wind direction is adjusted upward or downward by causing a Coanda effect on one curved surface of the air ventilation apparatus connected to a skin surface of a dashboard garnish and preventing the Coanda effect from being caused on an opposite surface. In addition to the Coanda effect, a structure for artificially forming a vortex is added to the air ventilation apparatus to maximize the Coanda effect.

Abstract: The present invention relates to a method for preparing a glutamate-based surfactant. More specifically, the present invention provides a method for preparing a glutamate-based surfactant, the method being very economical and being capable of mass production of the glutamate-based surfactant.

Abstract: Provided is an image recognition device. The image recognition device includes a frame data change detector that sequentially receives a plurality of frame data and detects a difference between two consecutive frame data, an ensemble section controller that sets an ensemble section in the plurality of frame data, based on the detected difference, an image recognizer that sequentially identifies classes respectively corresponding to a plurality of section frame data by applying different neural network classifiers to the plurality of section frame data in the ensemble section, and a recognition result classifier that sequentially identifies ensemble classes respectively corresponding to the plurality of section frame data by combining the classes in the ensemble section.

Abstract: The present invention relates to a method for preparing a glutamate-based surfactant. More specifically, the present invention provides a method for preparing a glutamate-based surfactant, the method being very economical and being capable of mass production of the glutamate-based surfactant.

Abstract: The neuromorphic arithmetic device comprises an input monitoring circuit that outputs a monitoring result by monitoring that first bits of at least one first digit of a plurality of feature data and a plurality of weight data are all zeros, a partial sum data generator that skips an arithmetic operation that generates a first partial sum data corresponding to the first bits of a plurality of partial sum data in response to the monitoring result while performing the arithmetic operation of generating the plurality of partial sum data, based on the plurality of feature data and the plurality of weight data, and a shift adder that generates the first partial sum data with a zero value and result data, based on second partial sum data except for the first partial sum data among the plurality of partial sum data and the first partial sum data generated with the zero value.

Abstract: An embodiment of the present invention provides a quantization method for weights of a plurality of batch normalization layers, including: receiving a plurality of previously learned first weights of the plurality of batch normalization layers; obtaining first distribution information of the plurality of first weights; performing a first quantization on the plurality of first weights using the first distribution information to obtain a plurality of second weights; obtaining second distribution information of the plurality of second weights; and performing a second quantization on the plurality of second weights using the second distribution information to obtain a plurality of final weights, and thereby reducing an error that may occur when quantizing the weight of the batch normalization layer.

Abstract: An embodiment of the present invention provides a neural network operator that performs a plurality of processes for each of a plurality of layers of a neural network, including: a memory that includes a data-storing space storing a plurality of data for performing the plurality of processes and a synapse code-storing space storing a plurality of descriptors with respect to the plurality of processes; a memory-transmitting processor that obtains the plurality of descriptors and transmits the plurality of data to the neural network operator based on the plurality of descriptors; an embedded instruction processor that obtains the plurality of descriptors from the memory-transmitting processor, transmits a first data set in a first descriptor to the neural network operator based on the first descriptor corresponding to the first process among the plurality of processes, reads a second descriptor corresponding to a second process, which is a next operation of the first process, based on the first descriptor, and

Abstract: An artificial neural network apparatus and an operating method including a plurality of layer processors for performing operations on input data are disclosed. The artificial neural network apparatus may include: a flag layer processor for outputting a flag according to a comparison result between a pooling output value of a current frame and a pooling output value of a previous frame; and a controller for stopping operation of a layer processor which performs operations after the flag layer processor among the plurality of layer processors when the flag is outputted from the flag layer processor, wherein the flag layer processor is a layer processor that performs a pooling operation first among the plurality of layer processors.

Abstract: An image sensor includes a pixel array and N analog-to-digital converters (ADCs). The pixel array includes N pixels arranged in each of a plurality of rows, and each of the N pixels include M photoelectric conversion elements. At least one of the N ADCs are shared by at least one of the M photoelectric conversion elements included in each of the N pixels.

Abstract: Disclosed is a location information determining method and system for providing a variety of services based on a location. The location information determining method includes receiving cell information; and determining location information that matches the cell information as location information of a mobile terminal from a location information database that stores location information that matches a plurality of pieces of cell information, respectively.

Abstract: Provided is a location based service providing method and system for automatically changing a setting of a mobile terminal based on a location of the mobile terminal determined based on cell information. A location based service providing method performed by a location based service providing system may include receiving information for verifying a location of a mobile terminal from the mobile terminal, determining location information that matches the information in a location information database as a current location of the mobile terminal, and changing a setting of the mobile terminal to be a user setting in response to the current location of the mobile terminal corresponding to a location registered in advance to apply the user setting.

Abstract: Disclosed is a location information determining method and system for providing a variety of services based on a location. The location information determining method includes receiving cell information; and determining location information that matches the cell information as location information of a mobile terminal from a location information database that stores location information that matches a plurality of pieces of cell information, respectively.

Abstract: An image sensor includes a pixel array and N analog-to-digital converters (ADCs). The pixel array includes N pixels arranged in each of a plurality of rows, and each of the N pixels include M photoelectric conversion elements. At least one of the N ADCs are shared by at least one of the M photoelectric conversion elements included in each of the N pixels.

Abstract: Provided is an artificial neural network device including pre-synaptic neurons configured to generate a plurality of input spike signals, and a post-synaptic neuron configured to receive the plurality of input spike signals and to generate an output spike signal during a plurality of time periods, wherein the post-synaptic neuron respectively applies different weights in the plurality of time periods according to contiguousness with a reference time period in which input spike signals, which lead generation of the output spike signal from among the plurality of input spike signals, are received.

Abstract: Provided is a design method of a compressed neural network system. The method includes generating a compressed neural network based on an original neural network model, analyzing a sparse weight among kernel parameters of the compressed neural network, calculating a maximum possible calculation throughput on a target hardware platform according to a sparse property of the sparse weight, calculating a calculation throughput with respect to access to an external memory on the target hardware platform according to the sparse property, and determining a design parameter on the target hardware platform by referring the maximum possible calculation throughput and the calculation throughput with respect to access.