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 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 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: 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: A multilayer ceramic electronic component includes: a ceramic body including an active portion having dielectric layers and first and second internal electrodes and first and second cover portions disposed on opposite surfaces of the active portion in a stacking direction, respectively; wherein when a region of the cover portion in contact with the first or second internal electrode is an inner region of the cover portion and a region of the active portion in contact with the inner region of the cover portion is an outer region of the active portion, 1.00<XA/XB<1.04 in which XA/XB is a ratio of a molar ratio (XA) of barium (Ba) to titanium (Ti) in the inner region of the cover portion to a molar ratio (XB) of barium (Ba) to titanium (Ti) in the outer region of the active portion.

Abstract: Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.

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: A method and apparatus for multi-level stepwise quantization for neural network are provided. The apparatus sets a reference level by selecting a value from among values of parameters of the neural network in a direction from a high value equal to or greater than a predetermined value to a lower value, and performs learning based on the reference level. The setting of a reference level and the performing of learning are iteratively performed until the result of the reference level learning satisfies a predetermined value and there is no variable parameter that is updated during learning among the parameters.

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 display device includes a flexible substrate including a display area and a non-display area. The substrate has a first surface and a second surface opposite to the first surface. A first protection film is disposed on the first surface of the substrate. The first protection film is disposed over the display area of the substrate. A second protection film is disposed on the first surface of the substrate. The second protection film is disposed over the non-display area of the substrate. A light transmittance of the first protection film is higher than that of the second protection film.

Abstract: A display device includes a flexible substrate including a display area and a non-display area. The substrate has a first surface and a second surface opposite to the first surface. A first protection film is disposed on the first surface of the substrate. The first protection film is disposed over the display area of the substrate. A second protection film is disposed on the first surface of the substrate. The second protection film is disposed over the non-display area of the substrate. A light transmittance of the first protection film is higher than that of the second protection film.

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: Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.

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: A device is disclosed.

Abstract: The present disclosure relates to a substrate processing apparatus and a substrate processing method. The substrate processing apparatus according to the exemplary embodiment of the present disclosure may include: a processing liquid supply tube; a nozzle unit which is supplied with a processing liquid from the processing liquid supply tube and discharges the processing liquid to the substrate; and a light source unit which is provided to irradiate the processing liquid discharged from the nozzle unit with ultraviolet rays. According to the present disclosure, the processing liquid, which is electrified while passing the processing liquid supply tube, is irradiated with ultraviolet rays, such that electricity is eliminated from the electrified processing liquid, and as a result, it is possible to minimize a problem that the substrate is contaminated by peripheral particles or arcing occurs on the substrate.

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 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