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 is provided.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

Abstract: An electronic apparatus, including a memory configured to store weight data used for computation of a neural network model; and a processor configured to: identify, from among weight values included in the weight data, at least one weight value having a size less than or equal to a threshold value, quantize remaining weight values other than the identified at least one weight value to obtain first quantized data including quantized values corresponding to the remaining weight values, identify, from among the quantized values, a quantized value closest to a predetermined value, obtain second quantized data including a quantized value corresponding to the at least one weight value based on the quantized value closest to the predetermined value, and store the first quantized data and the second quantized data in the memory

Abstract: Disclosed is an electronic device.

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: 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.

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 is provided.

Abstract: A data compression method and a data decompression method are provided. The method includes pruning an original data including a plurality of weight parameters, identifying at least one first weight parameter of which at least one first value is not changed by the pruning, among multiple weight parameters included in the pruned original data, and obtaining a first index data including location information of the at least one first weight parameter of which the at least one first value is not changed, identifying at least one second weight parameter of which at least one second value is changed by the pruning, among the multiple weight parameters included in the pruned original data, and substituting the at least one second weight parameter of which the at least one second value is changed with a don't care parameter.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

Abstract: A data compression method and a data decompression method are provided. The method includes pruning an original data including a plurality of weight parameters, identifying at least one first weight parameter of which at least one first value is not changed by the pruning, among multiple weight parameters included in the pruned original data, and obtaining a first index data including location information of the at least one first weight parameter of which the at least one first value is not changed, identifying at least one second weight parameter of which at least one second value is changed by the pruning, among the multiple weight parameters included in the pruned original data, and substituting the at least one second weight parameter of which the at least one second value is changed with a don't care parameter.