Abstract: An electronic device and a control method therefor are disclosed. An electronic device of the present disclosure includes a processor, which quantizes weight data with a combination of sign data and scaling factor data to obtain quantized data, and may input the first input data into a first module to obtain second input data in which exponents of input values included in the first input data are converted to the same value; input the second input data and the sign data into a second module to determine the signs of input values and perform calculations between the input values of which signs are determined to obtain first output data; input the first output data into a third module to normalize output values included in the first output data; and perform a multiplication operation on data including the normalized output values and the scaling factor data to obtain second output data.

Abstract: 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.

Abstract: 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.

Abstract: An electronic apparatus performing an operation of a neural network model is provided. The electronic apparatus includes a memory configured to store weight data including quantized weight values of the neural network model; and a processor configured to obtain operation data based on input data and binary data having at least one bit value different from each other, generate a lookup table by matching the operation data with the binary data, identify operation data corresponding to the weight data from the lookup table, and perform an operation of the neural network model based on the identified operation data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.