A computer-implemented method, computer program product and computing system for: processing an unencoded data file to identify a plurality of file segments; mapping each of the plurality of file segments to a portion of a dictionary file to generate a plurality of mappings, wherein each of the plurality of mappings includes a starting location and a length, thus generating a related encoded data file based, at least in part, upon the plurality of mappings; and transmitting the related encoded data file from a first location to a second location.

A computer-implemented method, computer program product and computing system for: encoding an unencoded data file to generate a related encoded data file based upon a plurality of mappings to a plurality of portions of a dictionary file; receiving a request to manipulate the unencoded data file, wherein the request would require a first sequence of operations to be performed on the unencoded data file; and processing the related encoded data file based, at least in part, upon the plurality of mappings and the dictionary file to generate a modified encoded data file, wherein processing the related encoded data file includes performing the first sequence of operations on the encoded data file to achieve the requested manipulations of the unencoded data file.

A computer-implemented method, computer program product and computing system for: processing an unencoded data file to identify a plurality of file segments wherein the unencoded data file is a dataset for use with a direct-coupled communication platform; mapping each of the plurality of file segments to a portion of a dictionary file to generate a plurality of mappings, wherein each of the plurality of mappings includes a starting location and a length, thus generating a related encoded data file based, at least in part, upon the plurality of mappings; and transmitting the related encoded data file from a first location to a second location using the direct-coupled communication platform.

A computer-implemented method, computer program product and computing system for: encoding an unencoded data file to generate a related encoded data file based, at least in part, upon a plurality of mappings to a plurality of portions of a dictionary file; receiving a request to manipulate the unencoded data file; and processing the related encoded data file based, at least in part, upon the plurality of mappings and the dictionary file to generate a modified encoded data file that represents the requested manipulations of the unencoded data file.

A memory system includes a storage device and a memory controller. The memory controller includes an encoder and a decoder. The encoder includes a first code table updating section configured to update the encoding code table and an encoding flow controlling section configured to control input to the first code table updating section by using a first data amount indicating a data amount of the input symbol. The first data amount is calculated based on the input symbol. The decoder includes a second code table updating section configured to update the decoding code table and a decoding flow controlling section configured to control input to the second code table updating section by using a second data amount indicating a data amount of the output symbol. The second data amount is calculated based on the output symbol in the same way as the calculation of the first data amount.

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.

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.

In an approach for character encoding, a processor receives a first query involving an attribute, wherein the first query utilizes a value encoded in accordance with a first encoding scheme. A processor identifies a table comprising values of the attribute in a compressed format. A processor creates at least one dictionary associated with the attribute, wherein the at least one new dictionary associated with the attribute maps a compressed value to a corresponding uncompressed value. A processor executes the first query by invoking a conversion operation in a query plan of the first query for decoding one or more queried values of the attribute. A processor fills the at least one new dictionary with one or more decoded query values to create at least one full dictionary. A processors stores the at least one full dictionary in a cache using a predefined cache management policy of the cache.

One example includes an integrated circuit (IC). The IC includes non-volatile memory and logic. The logic is configured to receive repair code associated with a memory instance and assign a compression parameter to the repair code based on a configuration of the memory instance. The logic is also configured to compress the repair code based on the compression parameter to produce compressed repair code and to provide compressed repair data that includes the compressed repair code and compression control data that identifies the compression parameter. A non-volatile memory controller is coupled between the non-volatile memory and the logic. The non-volatile memory controller is configured to transfer the compressed repair data to and/or from the non-volatile memory.

An input sequence that has a plurality of bits is received where the input sequence is associated with a first section of data within a compressed block. The plurality of bits in the input sequence are divided into a first sub-sequence comprising a first set of bits and a second sub-sequence comprising a second set of bits. The first sub-sequence is encoded using a first Huffman code tree to obtain a first codeword and the second sub-sequence is encoded using a second Huffman code tree to obtain a second codeword. Encoded data that includes information associated with the first Huffman code tree, information associated with the second Huffman code tree, the first codeword, and the second codeword is output.