A method of compressing weights of a neural network includes compressing a weight set including the weights of a the neural network, determining modified weight sets by changing at least one of the weights, calculating compression efficiency values for the determined modified weight sets based on a result of compressing the weight set and results of compressing the determined modified weight sets, determining a target weight of the weights satisfying a compression efficiency condition among the weights based on the calculated compression efficiency values, and determining a final compression result by compressing the weights based on a result of replacing the determined target weight.

A data processing platform, method, and program product perform compression and decompression of a set of data items. Suffix data and a prefix are selected for each respective data item in the set of data items based on data content of the respective data item. The set of data items is sorted based on the prefixes. The prefixes are encoded by querying multiple encoding tables to create a code word containing compressed information representing values of all prefixes for the set of data items. The code word and suffix data for each of the data items are stored in memory. The code word is decompressed to recover the prefixes. The recovered prefixes are paired with their respective suffix data.

A method of transforming a data file, the method executed by a processor. The method includes segmenting the data file into data segments and creating a bit index for each data segment having a size that is based on a configurable or preset data group unit. The method then involves indexing each data segment into its corresponding bit index by reading all data group unit values within the data segment and updating the bit index based on the read values, and generating an output data file or files comprising the bit indexes that represent the original data file.

A system collects statistical data for a data page, divides the data page into parts, analyzes the data page and the statistical data, based on compression efficiency of one or more compression methods for each part of each page, to determine a compression method for each part of page, and compresses, based on the analyzing, the parts of the data page.

Techniques for data processing may include: receiving a data chunk of the data set; determining, in accordance with criteria including a compressibility ratio for the data set and a cost ratio of compression computation cost and entropy computation cost, whether to activate or deactivate entropy computation for the data set, wherein the compressibility ratio is ratio of a number of compressible data chunks of the data set and a number of uncompressible data chunks of the data set; and responsive to determining to activate entropy computation for the data set, performing first processing comprising: determining an entropy value for the data chunk; and determining, in accordance with the entropy value for the data chunk, whether to compress the data chunk.

Disclosed embodiments relate to a convolutional neural network computing method and system based on weight kneading, comprising: arranging original weights in a computation sequence and aligning by bit to obtain a weight matrix, removing slack bits in the weight matrix, allowing essential bits in each column of the weight matrix to fill the vacancies according to the computation sequence to obtain an intermediate matrix, removing null rows in the intermediate matrix, obtain a kneading matrix, wherein each row of the kneading matrix serves as a kneading weight; obtaining positional information of the activation corresponding to each bit of the kneading weight; divides the kneading weight by bit into multiple weight segments, processing summation of the weight segments and the corresponding activations according to the positional information, and sending a processing result to an adder tree to obtain an output feature map by means of executing shift-and-add on the processing result.

A lossless data compressor of an aspect includes a first lossless data compressor circuitry coupled to receive input data. The first lossless data compressor circuitry is to apply a first lossless data compression approach to compress the input data to generate intermediate compressed data. The apparatus also includes a second lossless data compressor circuitry coupled with the first lossless data compressor circuitry to receive the intermediate compressed data. The second lossless data compressor circuitry is to apply a second lossless data compression approach to compress at least some of the intermediate compressed data to generate compressed data. The second lossless data compression approach different than the first lossless data compression approach. Lossless data decompressors are also disclosed, as are methods of lossless data compression and decompression.

According to one embodiment, a data decompression device decodes a code included in compressed data into a symbol. The data decompression device includes a first code length generation unit and a second code length generation unit. The first code length generation unit generates a first code length of a first code included in the compressed data by arithmetic calculation. The second code length generation unit generates a second code length of a second code by using a table. The second code is included in the compressed data. The second code is subsequent to the first code. The table indicates at least the first code and the second code length that is associated with the first code.

A system collects statistical data for a data page, divides the data page into parts, analyzes the data page and the statistical data, based on compression efficiency of one or more compression methods for each part of each page, to determine a compression method for each part of page, and compresses, based on the analyzing, the parts of the data page.

An electronic device that includes a decompression engine that includes N decoders and a decompressor decompresses compressed input data that includes N streams of data. Upon receiving a command to decompress compressed input data, the decompression engine causes each of the N decoders to decode a respective one of the N streams from the compressed input data separately and substantially in parallel with others of the N decoders. Each decoder outputs a stream of decoded data of a respective type for generating commands associated with a compression standard for decompressing the compressed input data. The decompressor next generates, from the streams of decoded data output by the N decoders, commands for decompressing the data using the compression standard to recreate the original data. The decompressor next executes the commands to recreate the original data and stores the original data in a memory or provides the original data to another entity.