Samsung Electronics Co., Ltd.

Sejung Kwon

Dongsoo Lee

Byeoungwook Kim

Baeseong Park

Parichay Kapoor

An electronic apparatus is provided. The electronic apparatus includes a memory configured to store one instruction or more and a processor configured to obtain output data by inputting input data to an artificial intelligence model including a plurality of layers by executing the instruction, and the artificial intelligence model is configured to output the output data based on operation through the plurality of layers and the processor is configured to encode operation data output from one of the plurality of layers and store the encoded operation data in the memory, obtain recovery data corresponding to the operation data by decoding the encoded operation data stored in the memory, and provide the obtained recovery data to another layer from among the plurality of layers.

Electronic apparatus for artificial intelligence model compression and method thereof

An electronic apparatus may include a memory configured to store compressed data that is to be decompressed for a neural network calculation of an artificial intelligence model; a decoder including a shift register configured to sequentially receive the compressed data in group units and output at least two groups of the compressed data, and a plurality of logic circuits configured to decompress the at least two groups of the compressed data to obtain decompressed data; and a processor configured to obtain the decompressed data in a form capable of being calculated by a neural network.

Electronic apparatus for decompressing a compressed artificial intelligence model and control method therefor

Provided is an electronic apparatus. The electronic apparatus includes a memory and a processor. The processor is configured to apply a low rank approximation using a matrix decomposition for a first square matrix among a plurality of square matrices based on parameter values of a deep learning model, and obtain a first approximated matrix and a second approximated matrix for the first square matrix, obtain second approximated matrices for each of a plurality of remaining square matrices other than the first square matrix among the plurality of square matrices, based on the first approximated matrix for the first square matrix, and store the first approximated matrix the first square matrix and the second approximated matrices for each of the plurality of square matrices in the memory.

Electronic apparatus and controlling method thereof

A method for an electronic apparatus to perform an operation of an artificial intelligence model includes acquiring resource information for hardware of the electronic apparatus while a plurality of data used for an operation of a neural network model are stored in a memory, the plurality of data respectively having degrees of importance different from each other; obtaining data to be used for the operation of the neural network model among the plurality of data according to the degrees of importance of each of the plurality of data based on the acquired resource information; and performing the operation of the neural network model by using the obtained data.

Electronic apparatus and method of performing operations thereof

An electronic device is provided. The electronic device includes a first memory configured to operate at a first speed and store compressed vectors corresponding to words, and scaling factors corresponding to the compressed vectors; a second memory that is faster than the first memory and is configured to store a first group of the compressed vectors, and store a first group of the scaling factors; and a processor configured to obtain a first compressed vector and a first scaling factor corresponding to an input word from the first memory or the second memory and process the obtained first compressed vector and the obtained first scaling factor by using a neural network.

Electronic device and operation method for embedding an input word using two memory operating speeds

A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.

Decompression apparatus for decompressing a compressed artificial intelligence model and control method thereof

An electronic apparatus is provided. The electronic apparatus includes sample data and memory storing a first matrix included in an artificial intelligence model trained based on sample data, and a processor configured to prunes each of a plurality of first elements included in the first matrix based on a first threshold, and acquire a first pruning index matrix that indicates whether each of the plurality of first elements has been pruned with binary data, factorize the first matrix to a second matrix of which size was determined based on the number of rows and the rank, and a third matrix of which size was determined based on the rank and the number of columns of the first matrix, prunes each of a plurality of second elements included in the second matrix based on a second threshold, and acquire a second pruning index matrix that indicates whether each of the plurality of second elements has been pruned with binary data, prunes each of a plurality of third elements included in the third matrix based on a third threshold, and acquire a third pruning index matrix that indicates whether each of the plurality of third elements has been pruned with binary data, acquire a final index matrix based on the second pruning index matrix and the third pruning index matrix, and update at least one of the second pruning index matrix or the third pruning index matrix by comparing the final index matrix with the first pruning index matrix.

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Suwon-si, South Korea

Sejung Kwon