Abstract: Disclosed is an operation method of a host which controls a computing device performing an artificial intelligence computation. The operation method includes receiving configuration information from the computation device, generating a plurality of lightening weight data by performing lightening on weight data based on the configuration information, generating a plurality of files by performing an aligning operation on the plurality of lightening weight data based on the configuration information, and loading the plurality of files into a memory device of the computing device. The configuration information includes channel information about a plurality of channels between the memory device and an accelerator, which are included in the computing device, and the number of the plurality of files is equal to the number of the plurality of channels.

Abstract: Disclosed is an accelerator performing an artificial intelligence (AI) computation, which includes a processing element that generates first result data by performing a first computation on first activation data and first weight data loaded from a memory, and a quantizer that generates first output data by performing a quantization on the first result data, and the first activation data, the first weight data, and the first output data are of a low precision type, the first result data is of a high precision type, and the first output data is stored in the memory.

Abstract: Disclosed is a machine learning accelerator which includes a first data controller that stores original length information indicating an original length, receives first data with a first length, and decompresses the first data with the first length to output second data with the original length, a second data controller that stores the original length information, receives third data with a second length shorter than the first length, and decompresses the third data with the second length to output fourth data with the original length, a first accelerator core that receives the second data with the original length from the first data controller and performs a first machine learning-based operation, and a second accelerator core that receives the fourth data with the original length from the second data controller and performs a second machine learning-based operation.

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: A matrix multiplier includes an input vector scaler configured to generate a first scaled input vector based on a first input vector and a plurality of quantization scale coefficients, a first data type converter configured to generate a first fixed-point scaled input vector based on the first scaled input vector, a processing element array including a first processing element configured to generate a first fixed-point output element based on the first fixed-point scaled input vector and first plurality of quantization sign values and a second processing element configured to generate a second fixed-point output element based on the first fixed-point scaled input vector and second plurality of quantization sign values, and a second data type converter configured to generate first and second output elements by converting data type of the first and second fixed-point output elements, and to output a first output vector including the first and second output elements.

Abstract: At least one embodiment provides a computing device including: a controller that receives first input data of a first data type and second input data of a second data type different from the first data type, and outputs a first signal representing the first data type, a second signal representing the second data type, and a clock signal based on the number of bits of the first input data and the second input data, and a computing circuit that performs a multiplication computation the first input data and the second input data based on the first signal, the second signal, and the clock signal and generates output data.

Abstract: Disclosed is an accelerator performing an accumulation operation on a plurality of data, each being a floating point type. A method of operating the accelerator includes loading first data, finding a first exponent, which is a maximum value among exponents of the first data, generating aligned first fractions by performing a bit shift on first fractions of the first data based on the first exponent, and generating a first accumulated value by an accumulation operation on the aligned first fractions, loading second data, finding a second exponent, which is a maximum value among exponents of the second data, and generating a first aligned accumulated value by a bit shift on the first accumulated value, generating aligned second fractions by a bit shift on second fractions of the second data, and generating a second accumulated value by an accumulation operation on the aligned second fractions and the first aligned accumulated value.

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.

Abstract: 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.

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: 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.

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 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, 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: 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: 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.

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.