Decompression and compression of neural network data using different compression schemes

    公开(公告)号:US11537853B1

    公开(公告)日:2022-12-27

    申请号:US16455258

    申请日:2019-06-27

    摘要: Described herein is a neural network accelerator (NNA) with a decompression unit that can be configured to perform multiple types of decompression. The decompression may include a separate subunit for each decompression type. The subunits can be coupled to form a pipeline in which partially decompressed results generated by one subunit are input for further decompression by another subunit. Depending on which types of compression were applied to incoming data, any number of the subunits may be used to produce a decompressed output. In some embodiments, the decompression unit is configured to decompress data that has been compressed using a zero value compression scheme, a shared value compression scheme, or both. The NNA can also include a compression unit implemented in a manner similar to that of the decompression unit.

    Decode device
    5.
    发明授权

    公开(公告)号:US11381250B2

    公开(公告)日:2022-07-05

    申请号:US17015874

    申请日:2020-09-09

    IPC分类号: H03M7/42 G06F3/06

    摘要: According to one embodiment, a dividing circuit divides a first bit string into second bit strings and outputs the divided second bit strings. The dividing circuit includes first, second, and third blocks. The first block executes first operation for each bit of a third bit string in the first bit string. The first operation is to calculate a head bit of a succeeding symbol when one bit is assumed to be a head of one symbol. The second block executes second operation for each bit of the third bit string for a set number of times. The second operation is to overwrite boundary information associated with one bit with boundary information associated with a bit indicated by the boundary information. The third block divides the third bit string immediately before a second bit indicated by boundary information associated with a first bit of the third bit string.

    Hardware friendly data compression

    公开(公告)号:US11184023B1

    公开(公告)日:2021-11-23

    申请号:US17000607

    申请日:2020-08-24

    摘要: Systems, apparatus and methods are provided for compressing data and decompressing compressed data. A method may comprise receiving an input data block to be compressed, generating a number of occurrences table and a cumulative occurrences table for distinct symbols in the input data block, for each symbol in the input data block, based on the number of occurrences table and the cumulative occurrences table, dynamically obtaining a number of shifts for right-shifting a current state “x” to encode a current symbol, outputting right-shifted bits to encoded data and obtaining a next state “x” and obtaining a final state “X” from a last state “x” generated in a final loop.

    Parallel processing of data having data dependencies for accelerating the launch and performance of operating systems and other computing applications

    公开(公告)号:US11151139B2

    公开(公告)日:2021-10-19

    申请号:US16900381

    申请日:2020-06-12

    申请人: Cornami, Inc.

    摘要: Representative embodiments are disclosed for a rapid and highly parallel decompression of compressed executable and other files, such as executable files for operating systems and applications, having compressed blocks including run length encoded (“RLE”) data having data-dependent references. An exemplary embodiment includes a plurality of processors or processor cores to identify a start or end of each compressed block; to partially decompress, in parallel, a selected compressed block into independent data, dependent (RLE) data, and linked dependent (RLE) data; to sequence the independent data, dependent (RLE) data, and linked dependent (RLE) data from a plurality of partial decompressions of a plurality of compressed blocks, to obtain data specified by the dependent (RLE) data and linked dependent (RLE) data, and to insert the obtained data into a corresponding location in an uncompressed file. The representative embodiments are also applicable to other types of data processing for applications having data dependencies.