A device configured to compress a tensor including a plurality of cells includes: a quadtree generator configured to generate a quadtree searching for a non-zero cell included in the tensor and extract at least one parameter value from the quadtree; a mode selector configured to determine a compression mode based on the at least one parameter; and a bitstream generator configured to generate a bitstream by compressing the tensor based on the compression mode.

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.

A system can identify a first group of time-series telemetry data that represents performance metrics of a computing device, wherein the first group of time-series telemetry data identifies respective first values and corresponding respective first timestamps. The system can create a second group of time-series telemetry data that identifies second timestamps. The system can populate the second group of time-series telemetry data with the respective first values at respective first locations of the second group of time-series telemetry data that correspond to the respective first timestamps of the respective first values. The system can create a tensor that identifies third timestamps. The system can populate the tensor with the respective first values at respective second locations of the tensor that correspond to the respective first timestamps of the respective first values, wherein populating the tensor comprises combining two values of the respective first values.

A method for managing a file system includes obtaining, by a compression optimizing manager, a compression algorithm selection request for the file system, determining a set of selection inputs based on a set of file system parameters of the file system, applying a compression selection model to the set of selection inputs to obtain a compression algorithm selection, and initiating a file system compression implementation of the file system using the compression algorithm selection.

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.

A method for differentiator-based compression of digital data includes (a) multiplying a tap-weight vector by an original data vector to generate a predicted signal, the original data vector comprising N sequential samples of an original signal, N being an integer greater than or equal to one, (b) using a subtraction module, subtracting the predicted signal from a sample of the original signal to obtain an error signal, (c) using a quantization module, quantizing the error signal to obtain a quantized error signal, and (d) updating the tap-weight vector according to changing statistical properties of the original signal.

Apparatus and methods for training a neural network accelerator using quantized precision data formats are disclosed, and in particular for storing activation values from a neural network in a compressed format having lossy or non-uniform mantissas for use during forward and backward propagation training of the neural network. In certain examples of the disclosed technology, a computing system includes processors, memory, and a compressor in communication with the memory. The computing system is configured to perform forward propagation for a layer of a neural network to produced first activation values in a first block floating-point format. In some examples, activation values generated by forward propagation are converted by the compressor to a second block floating-point format having a non-uniform and/or lossy mantissa. The compressed activation values are stored in the memory, where they can be retrieved for use during back propagation.

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.

Provided is a method for encoding scatterplot data using strings. The method may comprise receiving a plurality of data points in a data set. Each data point has at least a first data value corresponding to a first dimension and a second data value corresponding to a second dimension. The method further comprises determining a first resolution for the first dimension and a second resolution for the second dimension. The method further comprises determining an encoding scheme for encoding the plurality of data points. The encoding scheme includes a plurality of valid encoding characters. The method further comprises encoding each of the plurality of data points based on the first resolution, the second resolution, and the encoding scheme.

Disclosed are a method and apparatus for storing data. The method includes: acquiring data to be stored; converting the data to be stored from an initial data type to a target data type, a data length corresponding to the target data type being less than that corresponding to the initial data type; and storing the data to be stored of the target data type to a database. In the method according to the present disclosure, a storage space occupied by the data to be stored in the database is greatly reduced. In addition, the method according to the present disclosure is performed prior to lossy or lossless data compression storage of the data to be stored in the related art. That is, on the basis of a compression ratio when the data to be stored is stored in the related art, the present disclosure further improves a compression effect of the data to be stored by reducing the data length when the data to be stored is stored, and further saves storage resources of the database.