公开(公告)号：US20230259581A1

公开(公告)日：2023-08-17

申请号：US18109690

申请日：2023-02-14

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Won JEON ,  Young-Su KWON ,  Ju-Yeob KIM ,  Hyun-Mi KIM ,  Hye-Ji KIM ,  Chun-Gi LYUH ,  Mi-Young LEE ,  Jae-Hoon CHUNG ,  Yong-Cheol CHO ,  Jin-Ho HAN

IPC: G06F17/16 ,  G06F5/01

CPC classification number: G06F17/16 ,  G06F5/012

Abstract: Disclosed herein is a method for outer-product-based matrix multiplication for a floating-point data type includes receiving first floating-point data and second floating-point data and performing matrix multiplication on the first floating-point data and the second floating-point data, and the result value of the matrix multiplication is calculated based on the suboperation result values of floating-point units.

公开(公告)号：US20210303982A1

公开(公告)日：2021-09-30

申请号：US17205433

申请日：2021-03-18

Applicant: Electronics and Telecommunications Research Institute

Inventor： Mi Young LEE ,  Young-deuk JEON ,  Byung Jo KIM ,  Ju-Yeob KIM ,  Jin Kyu KIM ,  Ki Hyuk PARK ,  JOO HYUN LEE ,  MIN-HYUNG CHO

IPC: G06N3/063 ,  G06F7/544 ,  G06N3/08

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.

公开(公告)号：US20200226456A1

公开(公告)日：2020-07-16

申请号：US16742808

申请日：2020-01-14

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Young-deuk JEON ,  Byung Jo KIM ,  Ju-Yeob KIM ,  Jin Kyu KIM ,  Ki Hyuk PARK ,  Mi Young LEE ,  Joo Hyun LEE ,  Min-Hyung CHO

IPC: G06N3/063 ,  G06F17/15 ,  G06F7/575

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.

公开(公告)号：US20180225563A1

公开(公告)日：2018-08-09

申请号：US15868889

申请日：2018-01-11

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Ju-Yeob KIM ,  Byung Jo KIM ,  Jin Kyu KIM ,  Mi Young LEE ,  Seong Min KIM ,  Joo Hyun LEE

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/049 ,  G06N3/063 ,  G06N3/08

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.

公开(公告)号：US20180197084A1

公开(公告)日：2018-07-12

申请号：US15866351

申请日：2018-01-09

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Ju-Yeob KIM ,  Byung Jo KIM ,  Jin Kyu KIM ,  Mi Young LEE ,  Seong Min KIM ,  Joo Hyun LEE

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/084 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/063

Abstract: Provided is a convolutional neural network system. The system includes an input buffer configured to store an input feature, a parameter buffer configured to store a learning parameter, a calculation unit configured to perform a convolution layer calculation or a fully connected layer calculation by using the input feature provided from the input buffer and the learning parameter provided from the parameter buffer, and an output buffer configured to store an output feature outputted from the calculation unit and output the stored output feature to the outside. The parameter buffer provides a real learning parameter to the calculation unit at the time of the convolution layer calculation and provides a binary learning parameter to the calculation unit at the time of the fully connected layer calculation.

公开(公告)号：US20180131946A1

公开(公告)日：2018-05-10

申请号：US15806200

申请日：2017-11-07

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Mi Young LEE ,  Byung Jo KIM ,  Ju-Yeob KIM ,  Jin Kyu KIM ,  Seong Min KIM ,  Joo Hyun LEE

IPC: H04N19/169 ,  G06F17/30 ,  G06N3/08 ,  G06N3/063 ,  H04N19/13 ,  G06K9/62

CPC classification number: H04N19/169 ,  G06F16/50 ,  G06K9/6256 ,  G06K9/6267 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/08 ,  H04N19/13 ,  H04N19/48

Abstract: Provided is a convolution neural network system including an image database configured to store first image data, a machine learning device configured to receive the first image data from the image database and generate synapse data of a convolution neural network including a plurality of layers for image identification based on the first image data, a synapse data compressor configured to compress the synapse data based on sparsity of the synapse data, and an image identification device configured to store the compressed synapse data and perform image identification on second image data without decompression of the compressed synapse data.

公开(公告)号：US20180129935A1

公开(公告)日：2018-05-10

申请号：US15806111

申请日：2017-11-07

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Jin Kyu KIM ,  Byung Jo KIM ,  Seong Min KIM ,  Ju-Yeob KIM ,  Mi Young LEE ,  Joo Hyun LEE

IPC: G06N3/063 ,  G06N3/04 ,  G06F7/544

CPC classification number: G06N3/063 ,  G06F7/5443 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/082

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.