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.

An electronic device configured to perform polar coding is described. The electronic device includes a bit pattern generator (3403) configured to successively perform a bit pattern generation process over a series (t=┌n/w┐) of clock cycles; and a counter (c, 4203), operably coupled to the bit pattern generator (3403) and configured to count a number of successive bit pattern generation sub-processes over the series (t=┌n/w┐) of clock cycles. The bit pattern generator (3403) is configured to: provide a successive sub-set of (w) bits from a bit pattern vector (b.sub.k,n) in each successive t=┌n/w┐ clock cycle; where the bit pattern vector comprises n bits, of which ‘k’ bits adopt a first binary value and n−k bits adopt a complementary binary value.

Methods, devices, and computer programs are provided for character conversion. An original file is compressed, for instance, by a source or target device, to obtain a compressed file. Then, characters in the compressed file are converted from a source code page to a target code page to obtain a converted compressed file. The converted, compressed file may, where applicable, be sent to a target device. Also, the target device may decompress the converted compressed file to obtain a file in the target code page.

Methods and apparatus to parallelize data decompression are disclosed. An example method selecting initial starting positions in a compressed data bitstream; adjusting a first one of the initial starting positions to determine a first adjusted starting position by decoding the bitstream starting at a training position in the bitstream, the decoding including traversing the bitstream from the training position as though first data located at the training position is a valid token; outputting first decoded data generated by decoding a first segment of the bitstream starting from the first adjusted starting position; and merging the first decoded data with second decoded data generated by decoding a second segment of the bitstream, the decoding of the second segment starting from a second position in the bitstream and being performed in parallel with the decoding of the first segment, and the second segment preceding the first segment in the bitstream.

A method of data compression in which the total size of the compressed data is determined and based on that determination, the bit depth of the input data may be reduced before the data is compressed. The bit depth that is used may be determined by comparing the calculated total size to one or more pre-defined threshold values to generate a mapping parameter. The mapping parameter is then input to a remapping element that is arranged to perform the conversion of the input data and then output the converted data to a data compression element. The value of the mapping parameter may be encoded into the compressed data so that it can be extracted and used when subsequently decompressing the data.

A method, and the associated design, schema and techniques for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes of encryption/decryption or compression/decompression or both, including the use of Digital Lensing, Unlimited Code System, and other associated techniques. There is no assumption of or requirement for the digital information to be processed before processing.

Embodiments of the invention may be used to implement a rate converter that includes: 6 channels in forward (audio) path, each channel having a 24-bit signal path per channel, an End-to-end SNR of 110 dB, all within the 20 Hz to 20 KHz bandwidth. Embodiment may also be used to implement a rate converter having: 2 channels in a reverse path, such as for voice signals, 16-bit signal path per channel, an End-to-end SNR of 93 dB, all within 20 Hz to 20 KHz bandwidth. The rate converter may include sample rates such as 8, 11.025, 12, 16, 22.05, 24, 32 44.1, 48, and 96 KHz. Further, rate converters according to embodiments may include a gated clock in low-power mode to conserve power.

Embodiments of the invention may be used to implement a rate converter that includes: 6 channels in forward (audio) path, each channel having a 24-bit signal path per channel, an End-to-end SNR of 110 dB, all within the 20 Hz to 20 KHz bandwidth. Embodiment may also be used to implement a rate converter having: 2 channels in a reverse path, such as for voice signals, 16-bit signal path per channel, an End-to-end SNR of 93 dB, all within 20 Hz to 20 KHz bandwidth. The rate converter may include sample rates such as 8, 11.025, 12, 16, 22.05, 24, 32 44.1, 48, and 96 KHz. Further, rate converters according to embodiments may include a gated clock in low-power mode to conserve power.

Systems, apparatuses, and methods for compression of frequent data values across narrow links are disclosed. In one embodiment, a system includes a processor, a link interface unit, and a communication link. The link interface unit is configured to receive a data stream for transmission over the communication link, wherein the data stream is generated by the processor. The link interface unit determines if blocks of data of a first size from the data stream match one or more first data patterns and the link interface unit determines if blocks of data of a second size from the data stream match one or more second data patterns. The link interface unit sends, over the communication link, only blocks of data which do not match the first or second data patterns.

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.