Disclosed are a data compression method, a computer-readable storage medium, and an electronic device. The method includes: converting each data in a to-be-compressed data set into binary data in a preset format; determining a to-be-compressed bit and a significant bit for the each data in the to-be-compressed data set based on a sequence of all bits of the binary data; determining a compression bit width corresponding to the to-be-compressed data set based on bit widths of the significant bits; compressing the each data in the to-be-compressed data set based on the compression bit width, to obtain a compressed data set; and generating attribute information of the compressed data set. According to the present disclosure, the significant bit can be determined based on the sequence of all bits without adjusting orders of the bits of the binary data, thereby simplifying a data compression process and improving efficiency of data compression.

Disclosed is a processor chip that includes on-chip and off-chip software. The chip is optimized for hyperdimensional, fixed-point vector algebra to efficiently store, process, and retrieve information. A specialized on-chip data-embedding algorithm uses algebraic logic gates to convert off-chip normal data, such as images and spreadsheets, into discrete, abstract vector space where information is processed with off-chip software and on-chip accelerated computation via a desaturation method. Information is retrieved using an on-chip optimized decoding algorithm. Additional software provides an interface between a CPU and the processor chip to manage information processing instructions for efficient data transfer on- and off-chip in addition to providing intelligent processing that associates input information to allow for suggestive outputs.

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.

Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.

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 system and a method provide compression and decompression of weights of a layer of a neural network. For compression, the values of the weights are pruned and the weights of a layer are configured as a tensor having a tensor size of H×W×C in which H represents a height of the tensor, W represents a width of the tensor, and C represents a number of channels of the tensor. The tensor is formatted into at least one block of values. Each block is encoded independently from other blocks of the tensor using at least one lossless compression mode. For decoding, each block is decoded independently from other blocks using at least one decompression mode corresponding to the at least one compression mode used to compress the block; and deformatted into a tensor having the size of H×W×C.

A decoding device comprising a decoding unit configured to decode a tactile signal encoded for each of frequency bands. A decoding method comprising decoding a tactile signal encoded for each of frequency bands. A non-transitory storage medium encoded with instructions that, when executed by a computer, execute processing comprising decoding a tactile signal encoded for each of frequency bands.

Described herein is an apparatus and method for enhancing the dynamic range of an analog-to-digital converter (ADC). In one embodiment of the present approach, an analog input signal is amplified in a programmable gain amplifier (PGA) before the ADC receives the signal, so that the gain applied to an input signal, and gain (or attenuation) later applied in order to balance the overall gain of the circuit, occurs only in either the analog domain; in the prior art, gain occurs partly in each domain. The ADC gain is then adjusted to compensate for gain of the PGA and balance the overall gain of the circuit. In another embodiment, the ADC gain is adjusted, and gain of a digital gain element that receives the signal from the ADC is adjusted to compensate for the ADC gain and balance the overall gain of the circuit, eliminating the need for a PGA.

Techniques and apparatus for parallel data compression are described. An apparatus to provide parallel data compression may include at least one memory and logic for a compression component, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to provide at least one data input sequence to a plurality of compression components, determine compression information for the plurality of compression components, and perform a compression process on the at least one data input sequence via the plurality of compression components to generate at least one data output sequence, the plurality of compression components to perform the compression process in parallel based on the compression information.

A data compression method using the range variant of asymmetric numeral systems to encode a data stream, where the probability distribution table is constructed using a Markov model. This type of encoding results in output that has higher compression ratios when compared to other compression algorithms and it performs particularly well with information that represents gene sequences or information related to gene sequences.