Apparatuses, systems, and techniques to decompress data in parallel. In at least one embodiment, decompressing a variable-length-coded data stream speculatively decodes overlapping portions of said data stream to determine locations to begin correctly decoding said data stream.

A data output method, a data acquisition method, a device, and an electronic apparatus are provided, and a specific technical solution is: reading a first data sub-block, and splicing the first data sub-block into a continuous data stream, wherein the first data sub-block is a data sub-block in transferred data in a neural network; compressing the continuous data stream to acquire a second data sub-block; determining, according to a length of the first data sub-block and a length of the second data sub-block, whether there is a gain in compression of the continuous data stream; outputting the second data sub-block if there is the gain in the compression of the continuous data stream.

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.

A decoder includes circuitry configured to receive a bitstream identify, in the bitstream, a current frame, wherein the current frame includes a first region and a third region, detect, in the bitstream, an indication that the first region is encoded according to a lossless encoding protocol, and decode the current frame, wherein decoding the current frame further comprises decoding the first region using a lossless decoding protocol corresponding to the lossless encoding protocol.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

A FRAMEWORK and the associated method, schema and design for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes including the purposes of encryption/decryption or compression/decompression or both. There is no assumption of the digital information to be processed before processing. A Universal Coder is invented and now Pigeonhole meets Blackhole.

Encoding devices, methods and programs that encode with high transmission efficiency by controlling a running disparity are disclosed. In one example, an encoding device includes a scrambling circuit that scrambles an input data string, a calculation circuit that calculates a first running disparity of the scrambled data string, a determination circuit that determines whether or not to invert the scrambled data string on the basis of a first running disparity calculated by the calculation circuit and a second running disparity calculated at a time point before the first running disparity, and an addition circuit that inverts or non-inverts the scrambled data string on the basis of a determination result by the determination circuit, adds a flag indicating the determination result, and outputs the data string. The technology can be applied to devices that perform SLVS-EC standard communication.

A weight data compression method includes: generating a 4-bit data string of 4-bit data items each expressed as any one of nine 4-bit values, by dividing ternary weight data into data items each having 4 bits; and generating first compressed data including a first flag value string and a first non-zero value string by (i) generating the first flag value string by assigning one of 0 and 1 as a first flag value of a 1-bit flag to a 4-bit data item 0000 and assigning an other of 0 and 1 as a second flag value of the 1-bit flag to a 4-bit data item other than 0000 among the 4-bit data items in the 4-bit data string and (ii) generating the first non-zero value string by converting the 4-bit data item other than 0000 into a 3-bit data item having any one of eight 3-bit values.

A system and method for highly efficient encoding of data that includes extended functionality for asymmetric encoding/decoding and network policy enforcement. In the case of asymmetric encoding/decoding the original data is encoded by an encoder according to a codebook and sent to a decoder, but the output of the decoder depends on data manipulation rules applied at the decoding stage to transform the decoded data into a different data set from the original data. In the case of network policy enforcement, a behavior appendix into the codebook, such that the encoder and/or decoder at each node of the network comply with network behavioral rules, limits, and policies during encoding and decoding.

Aspects of the disclosure provide methods and apparatuses for neural network model compression/decompression. In some examples, an apparatus for neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry decodes, from a bitstream corresponding to a representation of a neural network, at least a syntax element to be applied to multiple blocks in the neural network. Then, the processing circuitry reconstructs, from the bitstream, weight coefficients in the blocks based on the syntax element.