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.

Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.

Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.

A three-dimensional data encoding method includes: generating an N-ary tree structure of three-dimensional points included in three-dimensional data, where N is an integer greater than or equal to 2; generating first encoded data by encoding a first branch using a first encoding process, the first branch having, as a root, a first node included in a first layer that is one of layers included in the N-ary tree structure; generating second encoded data by encoding a second branch using a second encoding process different from the first encoding process, the second branch having, as a root, a second node included in the first layer and different from the first node; and generating a bitstream including the first encoded data and the second encoded data.

A three-dimensional data encoding method includes: generating an N-ary tree structure of three-dimensional points included in three-dimensional data, where N is an integer greater than or equal to 2; generating first encoded data by encoding a first branch using a first encoding process, the first branch having, as a root, a first node included in a first layer that is one of layers included in the N-ary tree structure; generating second encoded data by encoding a second branch using a second encoding process different from the first encoding process, the second branch having, as a root, a second node included in the first layer and different from the first node; and generating a bitstream including the first encoded data and the second encoded data.

A system comprises an encoder configured to entropy encode a bitstream comprising both compressible and non-compressible symbols. The encoder parses the bitstream into a compressible symbol sub-stream and a non-compressible sub-stream. The non-compressible symbol sub-stream bypass an entropy encoding component of the encoder while the compressible symbol sub-stream is entropy encoded. When a quantity of bytes of entropy encoded symbols and bypass symbols is accumulated a chunk of fixed or known size is formed using the accumulated entropy encoded symbol bytes and the bypass bytes without waiting on the full bitstream to be processed by the encoder. In a complementary manner, a decoder reconstructs the bitstream from the packets or chunks.

A system comprises an encoder configured to entropy encode a bitstream comprising both compressible and non-compressible symbols. The encoder parses the bitstream into a compressible symbol sub-stream and a non-compressible sub-stream. The non-compressible symbol sub-stream bypass an entropy encoding component of the encoder while the compressible symbol sub-stream is entropy encoded. When a quantity of bytes of entropy encoded symbols and bypass symbols is accumulated a chunk of fixed or known size is formed using the accumulated entropy encoded symbol bytes and the bypass bytes without waiting on the full bitstream to be processed by the encoder. In a complementary manner, a decoder reconstructs the bitstream from the packets or chunks.

A system utilizing a high throughput coding mode for CABAC in HEVC is described. The system may include an electronic device configured to obtain a block of data to be encoded using an arithmetic based encoder; to generate a sequence of syntax elements using the obtained block; to compare an Absolute-3 value of the sequence or a parameter associated with the Absolute-3 value to a preset value; and to convert the Absolute-3 value to a codeword using a first code or a second code that is different than the first code, according to a result of the comparison.

A system and method to compress application control data, such as weights for a layer of a convolutional neural network, is disclosed. A multi-core system for executing at least one layer of the convolutional neural network includes a storage device storing a compressed weight matrix of a set of weights of the at least one layer of the convolutional network and a decompression matrix. The compressed weight matrix is formed by matrix factorization and quantization of a floating point value of each weight to a floating point format. A decompression module is operable to obtain an approximation of the weight values by decompressing the compressed weight matrix through the decompression matrix. A plurality of cores executes the at least one layer of the convolutional neural network with the approximation of weight values to produce an inference output.

A system and method to compress application control data, such as weights for a layer of a convolutional neural network, is disclosed. A multi-core system for executing at least one layer of the convolutional neural network includes a storage device storing a compressed weight matrix of a set of weights of the at least one layer of the convolutional network and a decompression matrix. The compressed weight matrix is formed by matrix factorization and quantization of a floating point value of each weight to a floating point format. A decompression module is operable to obtain an approximation of the weight values by decompressing the compressed weight matrix through the decompression matrix. A plurality of cores executes the at least one layer of the convolutional neural network with the approximation of weight values to produce an inference output.