A method for encoding a sequence of records, each record of said sequence of records comprising a plurality of different fields, said different fields being identical for each record of said sequence of records, said method comprising selecting an encoding algorithm for each field of said plurality of fields such that said each field is associated with a selected encoding algorithm; encoding data of said each field using said selected encoding algorithm to determine encoded field data for said each field for said each record; and for said each record, interleaving said encoded field data for said each field to produce an encoded sequence of said records wherein said encoded field data are interleaved for said each record.

An accelerator device determines a compression format based on a header of a structured data element to be decompressed. The accelerator device may configure the accelerator device based on the compression format. The accelerator device may decompress a data block of the structured data element based on the configuration.

Systems, methods, and apparatuses are provided for compressing values. A plurality of parameters may be obtained from a memory, each parameter comprising a floating-point number that is used in a relationship between artificial neurons or nodes in a model. A mantissa value and an exponent value may be extracted from each floating-point number to generate a set of mantissa values and a set of exponent values. The set of mantissa values may be compressed to generate a mantissa lookup table (LUT) and a plurality of mantissa LUT index values. The set of exponent values may be encoded to generate an exponent LUT and a plurality of exponent LUT index values. The mantissa LUT, mantissa LUT index values, exponent LUT, and exponent LUT index values may be provided to one or more processing entities to train the model.

A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

A data model is trained to determine whether data is raw, compressed, and/or encrypted. The data model may also be trained to recognize which compression algorithm was used to compress data and predict compression ratios for the data using different compression algorithms. A storage system uses the data model to independently identify raw data. The raw data is grouped based on similarity of statistical features and group members are compressed with the same compression algorithm and may be encrypted after compression with the same encryption algorithm. The data model may also be used to identify sub-optimally compressed data, which may be uncompressed and grouped for compression using a different compression algorithm.

Video coding systems or apparatus utilizing context-based adaptive binary arithmetic coding (CABAC) during encoding and/or decoding, are configured according to the invention with an enhanced binarization of non-zero Delta-QP (dQP). During binarization the value of dQP and the sign are separately encoded using unary coding and then combined into a binary string which also contains the dQP non-zero flag. This invention capitalizes on the statistical symmetry of positive and negative values of dQP and results in saving bits and thus a higher coding efficiency.

A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

A system and method for compressing sensor data at an edge node of a distributed computing network. The method includes training the edge node to with a plurality of known signal templates. Each known signal template corresponding to a corresponding one of a plurality of events observable by the sensor. A raw data signal is collected by a sensor of the edge node. The raw data signal is classified to one of the known signal templates based on a degree of similarity between the raw data signal and the known signal template. A compression scheme is selected based on the classification of the raw data signal. The raw data signal is compressed in accordance with the compression scheme.

Video coding systems or apparatus utilizing context-based adaptive binary arithmetic coding (CABAC) during encoding and/or decoding, are configured according to the invention with an enhanced binarization of non-zero Delta-QP (dQP). During binarization the value of dQP and the sign are separately encoded using unary coding and then combined into a binary string which also contains the dQP non-zero flag. This invention capitalizes on the statistical symmetry of positive and negative values of dQP and results in saving bits and thus a higher coding efficiency.

A system and method for compressing sensor data at an edge node of a distributed computing network. The method includes training the edge node to with a plurality of known signal templates. Each known signal template corresponding to a corresponding one of a plurality of events observable by the sensor. A raw data signal is collected by a sensor of the edge node. The raw data signal is classified to one of the known signal templates based on a degree of similarity between the raw data signal and the known signal template. A compression scheme is selected based on the classification of the raw data signal. The raw data signal is compressed in accordance with the compression scheme.