A method for computational storage may include receiving, at a storage device, a modified version of a portion of data, generating, at the storage device, a restored portion of data from the modified version of the portion of data, and performing, at the storage device, an operation on the restored portion of data. The method may further include receiving, at the storage device, a request to perform the operation on the portion of data. The generating may include decompressing the modified version of the portion of data. The generating may include decrypting the modified version of the portion of data. The method may further include sending, from the storage device, a result of the operation on the restored portion of data. The operation may include a filtering operation. The operation may include a scanning operation. The method may further include dividing data to generate the portion of data.

Example embodiments relate to using a multi-context entropy coder for encoding adjacency lists. A system may obtain a graph having data (or multiple graphs) and may compress the data of the graph using a multi -context entropy coder. The multi-context entropy coder may encode adjacency lists within the data such that each integer is assigned to a different probability distribution. For example, operating the multi-context entropy coder may involve using a combination of arithmetic coding, Huffman coding, and ANS. The assignment of integers to the probability distributions may depend on each integer’s role and/or previous values of a similar kind. By using multi -context entropy- coding, the computing system may increase compression ratio while maintaining similar processing speed.

A method of encoding input data includes dividing the input data into a plurality of data packets, an input packet of the plurality of data packets including a plurality of digits in a first base system, base-converting the input packet from the first base system to generate a base-converted packet including a plurality of converted digits in a second base system, the second base system having a base value lower than that of the first base system, and incrementing the converted digits to generate a coded packet for transmission through a communication channel.

A system and method for compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

In accordance with an embodiment, the method includes determining a second sequence of numbers of digits for encoding the respective integer coefficient values of the first sequence, the second sequence including, as first element, a first number of digits for encoding the first integer coefficient value of the first sequence, and as second and subsequent elements, constrained numbers of digits that are greater than or equal to respective minimum required numbers of digits for encoding the second and subsequent integer coefficient values of the first sequence. The constrained numbers of digits are such that any two successive elements of the second sequence do not differ from each other by more than a given threshold value. The method further includes encoding difference values between the successive elements of the second sequence; and encoding the integer coefficient values of the first sequence using the respective numbers of digits of the second sequence.

A processor includes a memory hierarchy, buffer, and a decompressor. The decompressor includes circuitry to read elements to be decompressed according to a compression scheme, parse the elements to identify literals and matches, and, with the literals and matches, generate an intermediate token stream formatted for software-based copying of the literals and matches to produce decompressed data. The intermediate token stream is to include a format for multiple tokens that are to be written in parallel with each other, and another format for tokens that include a data dependency upon themselves.

To speed up decoding of a range code. A decompression circuit calculates a plurality of candidate bit values for each bit of the N-bit string based on a plurality of possible bit histories of a bit before a K-th bit in parallel for a plurality of bits, and repeatedly selects a correct bit value of the K-th bit from the plurality of candidate bit values based on a correct bit history of the bit before the K-th bit to decode the N-bit string.

A data compressor a zero-value remover, a zero bit mask generator, a non-zero values packer, and a row-pointer generator. The zero-value remover receives 2.sup.N bit streams of values and outputs 2.sup.N non-zero-value bit streams having zero values removed from each respective bit stream. The zero bit mask generator receives the 2.sup.N bit streams of values and generates a zero bit mask for a predetermined number of values of each bit stream in which each zero bit mask indicates a location of a zero value in the predetermined number of values corresponding to the zero bit mask. The non-zero values packer receives the 2.sup.N non-zero-value bit streams and forms a group of packed non-zero values. The row-pointer generator that generates a row-pointer for each group of packed non-zero values.

A method is disclosed to reduce computation in a self-attention deep-learning model. A feature-map regularization term is added to a loss function while training the self-attention model. At least one low-magnitude feature is removed from at least one feature map of the self-attention model during inference. Weights of the self-attention model are quantized after the self-attention model has been trained. Adding the feature-map regularization term reduces activation values of feature maps, and removing the at least one low-magnitude feature from at least one feature map may be performed by setting the low-magnitude feature to be equal to zero based on the low-magnitude feature having a value that is less than a predetermined threshold. Feature maps of the self-attention model quantized and compressed.

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.