An electronic device includes at least one compression-decompression functional block and a hierarchy of cache memories with a first cache memory and a second cache memory. The at least one compression-decompression functional block receives data in an uncompressed state, compresses the data using one of a first compression or a second compression, and, after compressing the data, provides the data to the first cache memory for storage therein. When the data is retrieved from the first cache memory to be stored in the second cache memory, when the data is compressed using the first compression, the compression-decompression functional block decompresses the data to reverse effects of the first compression on the data, thereby restoring the data to the uncompressed state and provides the data compressed using the second compression or in the uncompressed state to the second cache memory for storage therein.

A matrix compression/decompression accelerator (MCA) system/method that coordinates lossless data compression (LDC) and lossless data decompression (LDD) transfers between an external data memory (EDM) and a local data memory (LDM) is disclosed. The system implements LDC using a 2D-to-1D transformation of 2D uncompressed data blocks (2DU) within LDM to generate 1D uncompressed data blocks (1DU). The 1DU is then compressed to generate a 1D compressed superblock (CSB) in LDM. This LDM CSB may then be written to EDM with a reduced number of EDM bus cycles. The system implements LDD using decompression of CSB data retrieved from EDM to generate a 1D decompressed data block (1DD) in LDM. A 1D-to-2D transformation is then applied to the LDM 1DD to generate a 2D decompressed data block (2DD) in LDM. This 2DD may then be operated on by a matrix compute engine (MCE) using a variety of function operators.

One aspect of the disclosure provides a device, comprising: an allocation module, for determining one or more metrics of each of a plurality of data streams; a compression module, for compressing each of the plurality of data streams and generating a plurality of compressed data streams, the compression module applying a compression ratio that varies as a function of the metrics determined by the allocation module; and a buffer memory, for storing the plurality of compressed data streams.

Various embodiments include methods and devices for implementing compression of high dynamic ratio fields. Various embodiments may include receiving a compression block having data units, receiving a mapping for the compression block, wherein the mapping is configured to map bits of each data unit to two or more data fields to generate a first set of data fields and a second set of data fields, compressing the first set of data fields together to generate a compressed first set of data fields, and compressing the second set of data fields together to generate a compressed second set of data fields.

A method for more efficiently utilizing data compression in block-level storage systems is disclosed. In one embodiment, such a method includes receiving, by a storage system, I/O operations from a host system. The storage system determines, at a selected interval, a compression rate of data associated with the I/O operations. Using the compression rate determined at the selected interval, the storage system updates an average compression rate. The storage system then determines whether the average compression rate is above a threshold. If the average compression rate is above the threshold, the storage system compresses data associated with I/O operations from the host system. If the average compression rate is not above the threshold, the storage system does not compress data associated with I/O operations from the host system. A corresponding system and computer program product are also disclosed.

A computer-implemented method includes: obtaining input data comprising a plurality of data values, wherein each data value is associated with a time value; obtaining a plurality of threshold criteria; and (a) selecting, or generating, a dataset or a plurality of datasets that are different to one another from the input data, and determining whether each selected, or generated dataset meets a threshold criterion of the plurality of threshold criteria; and (b) causing each selected, or generated, dataset that meets the threshold criterion to be stored in memory in association with the threshold criterion, repeating (a) and (b) for each threshold criterion of the plurality of threshold criteria, thus causing a plurality of datasets to be stored in the memory, wherein each stored dataset meets a threshold criterion of the plurality of threshold criteria.

A matrix compression/decompression accelerator (MCA) system/method that coordinates lossless data compression (LDC) and lossless data decompression (LDD) transfers between an external data memory (EDM) and a local data memory (LDM) is disclosed. The system implements LDC using a 2D-to-1D transformation of 2D uncompressed data blocks (2DU) within LDM to generate 1D uncompressed data blocks (1DU). The 1DU is then compressed to generate a 1D compressed superblock (CSB) in LDM. This LDM CSB may then be written to EDM with a reduced number of EDM bus cycles. The system implements LDD using decompression of CSB data retrieved from EDM to generate a 1D decompressed data block (1DD) in LDM. A 1D-to-2D transformation is then applied to the LDM 1DD to generate a 2D decompressed data block (2DD) in LDM. This 2DD may then be operated on by a matrix compute engine (MCE) using a variety of function operators.

The systems and methods described herein can pre-process a blendshape matrix via a global clusterization process and a local clusterization process. The pre-processing can cause the blendshape matrix to be divided into multiple blocks. The techniques can further apply a matrix compression technique to each block of the blendshape matrix to generate a compression result. The matrix compression technique can comprise a matrix approximation step, an accuracy verification step, and a recursive compression step. The compression result for each block may be combined to generate a compressed blendshape matrix for rendering a virtual entity.

A device and a method for compressing data by a device are provided, which relate to the storage field and are used to resolve a prior-art problem that a compression ratio at which data in a data block is compressed by a device is relatively low. The method includes: parsing, by a device, an information block in a data block, to obtain a file type of data in the data block and a data sub-block that is included in the data block; determining a characteristic of data in the data sub-block according to the file type; selecting, according to the characteristic, a target compression algorithm that is used to compress the data in the data sub-block; and compressing the data in the data sub-block by using the target compression algorithm. Embodiments of the present disclosure are used to compress data.

One aspect of the disclosure provides a device, comprising: an allocation module, for determining one or more metrics of each of a plurality of data streams; a compression module, for compressing each of the plurality of data streams and generating a plurality of compressed data streams, the compression module applying a compression ratio that varies as a function of the metrics determined by the allocation module; and a buffer memory, for storing the plurality of compressed data streams.