The disclosure is directed at a method of data compression using inferred data. By determining the number of leading zeroes for each data structure, a general header presenting all leading zeros can be generated and use to compress the data.

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.

The present disclosure relates to a system and method for compressing a dataset. The dataset can be divided in to a plurality of groups. Each group can be converted independently into corresponding text file using dictionary coding technique. The corresponding text files can be compressed independently into corresponding compressed files. Finally, all the corresponding compressed files can be combined together to generate a complete compressed data.

The present disclosure relates to a system and method for compressing a dataset. The dataset can be divided in to a plurality of groups. Each group can be converted independently into corresponding text file using dictionary coding technique. The corresponding text files can be compressed independently into corresponding compressed files. Finally, all the corresponding compressed files can be combined together to generate a complete compressed data.

Methods, apparatus, systems and articles of manufacture to compress data are disclosed. An example apparatus includes a data slicer to split a dataset into a plurality of blocks of data; a data processor to select a first compression technique for a first block of the plurality of blocks of data based on first characteristics of the first block; and select a second compression technique for a second block of the plurality of blocks of data based on second characteristics of the second block; a first compressor to compress the first block using the first compression technique to generate a first compressed block of data; a second compressor to compress the second block using the second compression technique to generate a second compressed block of data; and a header generator to generate a first header identifying the first compression technique and a second header identifying the second compression technique.

Methods, apparatus, systems and articles of manufacture to compress data are disclosed. An example apparatus includes a data slicer to split a dataset into a plurality of blocks of data; a data processor to select a first compression technique for a first block of the plurality of blocks of data based on first characteristics of the first block; and select a second compression technique for a second block of the plurality of blocks of data based on second characteristics of the second block; a first compressor to compress the first block using the first compression technique to generate a first compressed block of data; a second compressor to compress the second block using the second compression technique to generate a second compressed block of data; and a header generator to generate a first header identifying the first compression technique and a second header identifying the second compression technique.

A method and apparatus of a device that compresses an object stored in memory is described. In an exemplary embodiment, the device receives an indication that the object is to be compressed. The device further selects one of a plurality of compression algorithms based on at least a characteristic of the object. In addition, the device compresses the object in-memory using the selected compression algorithm.

A method and apparatus of a device that compresses an object stored in memory is described. In an exemplary embodiment, the device receives an indication that the object is to be compressed. The device further selects one of a plurality of compression algorithms based on at least a characteristic of the object. In addition, the device compresses the object in-memory using the selected compression algorithm.

Optimized memory usage and management is crucial to the overall performance of a neural network (NN) or deep neural network (DNN) computing environment. Using various characteristics of the input data dimension, an apportionment sequence is calculated for the input data to be processed by the NN or DNN that optimizes the efficient use of the local and external memory components. The apportionment sequence can describe how to parcel the input data (and its associated processing parameters—e.g., processing weights) into one or more portions as well as how such portions of input data (and its associated processing parameters) are passed between the local memory, external memory, and processing unit components of the NN or DNN. Additionally, the apportionment sequence can include instructions to store generated output data in the local and/or external memory components so as to optimize the efficient use of the local and/or external memory components.

Optimized memory usage and management is crucial to the overall performance of a neural network (NN) or deep neural network (DNN) computing environment. Using various characteristics of the input data dimension, an apportionment sequence is calculated for the input data to be processed by the NN or DNN that optimizes the efficient use of the local and external memory components. The apportionment sequence can describe how to parcel the input data (and its associated processing parameters—e.g., processing weights) into one or more portions as well as how such portions of input data (and its associated processing parameters) are passed between the local memory, external memory, and processing unit components of the NN or DNN. Additionally, the apportionment sequence can include instructions to store generated output data in the local and/or external memory components so as to optimize the efficient use of the local and/or external memory components.