A technique provides efficient data protection, such as erasure coding, for data blocks of volumes served by storage nodes of a cluster. Data blocks associated with write requests of unpredictable client workload patterns may be compressed. A set of the compressed data blocks may be selected to form a write group and an erasure code may be applied to the group to algorithmically generate one or more encoded blocks in addition to the data blocks. Due to the unpredictability of the data workload patterns, the compressed data blocks may have varying sizes. A pool of the various-sized compressed data blocks may be established and maintained from which the data blocks of the write group are selected. Establishment and maintenance of the pool enables selection of compressed data blocks that are substantially close to the same size and, thus, that require minimal padding.

To generate an optimum compressor irrespective of the number of dimensions and a format of a multidimensional dataset. A storage system refers to dimension setting information, which is information representing an attribute for each of data dimensions of the multidimensional dataset, and generates a compressor based on the dimension setting information.

Systems and methods are provided for reducing error in data compression and decompression when data is transmitted over low bandwidth communication links, such as satellite links. Embodiments of the present disclosure provide systems and methods for variable block size compression for gridded data, efficiently storing null values in gridded data, and eliminating growth of error in compressed time series data.

A method of integrated circuit includes: providing a non-volatile memory circuit for securely and permanently recording and protecting key data content having Y bits; providing a programmable memory circuit for storing user configuration data content having X bits greater than Y bits; converting the user configuration data content having X bits into user configuration key content having Y bits; comparing the user configuration key content having Y bits with the key data content having Y bits; selecting fallback configuration data content having X bits as output data when the user configuration key content does not match the key data content; selecting the user configuration data content having X bits as the output data when the user configuration key content matches the key data content; and receiving the output data of the decision circuit and performing at least one corresponding capability operation according to the output data.

An abnormality detection device according to an embodiment includes a detector, a remover, and a learner. The detector detects first abnormal data in detection target data which is an abnormality detection target by inputting the detection target data to a first autoencoder which performed learning based on first learning target data which is a learning target. The remover removes data associated with the first abnormal data from the first learning target data to generate second learning target data by inputting the first learning target data to a second autoencoder which performed learning based on the first abnormal data detected by the detector. The learner causes the first autoencoder to perform learning based on the second learning target data generated by the remover.

A method of compressing weights of a neural network includes compressing a weight set including the weights of a the neural network, determining modified weight sets by changing at least one of the weights, calculating compression efficiency values for the determined modified weight sets based on a result of compressing the weight set and results of compressing the determined modified weight sets, determining a target weight of the weights satisfying a compression efficiency condition among the weights based on the calculated compression efficiency values, and determining a final compression result by compressing the weights based on a result of replacing the determined target weight.

Methods, apparatus, systems, and software for implementing self-checking compression. A byte stream is encoded to generate tokens and selected tokens are encoded with hidden parity information in a compressed byte stream that may be stored for later streaming or streamed to a receiver. As the compressed byte stream is received, it is decompressed, with the hidden parity information being decoded and used to detect for errors in the decompressed data, enabling errors to be detected on-the-fly rather than waiting to perform a checksum over an entire received file. In one embodiment the byte stream is encoded using a Lempel-Ziv 77 (LZ77)-based encoding process to generate a sequence of tokens including literals and references, with all or selected references encoded with hidden parity information in a compressed byte stream having a standard format such as DEFLATE or Zstandard. The hidden parity information is encoded such that the compressed byte stream may be decompressed without parity checks using standard DEFLATE or Zstandard decompression schemes. Dictionary coders such as LZ78 and LZW may also be used.

Systems and methods are disclosed for reducing latency and power consumption of on-chip movement through an approximate communication framework for network-on-chips (“NoCs”). The technology leverages the fact that big data applications (e.g., recognition, mining, and synthesis) can tolerate modest error and transfers data with the necessary accuracy, thereby improving the energy-efficiency and performance of multi-core processors.

Methods and apparatus relating to verifying a compressed stream fused with copy or transform operation(s) are described. In an embodiment, compression logic circuitry compresses input data and stores the compressed data in a temporary buffer. The compression logic circuitry determines a first checksum value corresponding to the compressed data stored in the temporary buffer. Decompression logic circuitry performs a decompress-verify operation and a copy operation. The decompress-verify operation decompresses the compressed data stored in the temporary buffer to determine a second checksum value corresponding to the decompressed data from the temporary buffer. The copy operation transfers the compressed data from the temporary buffer to a destination buffer in response to a match between the first checksum value and the second checksum value. Other embodiments are also disclosed and claimed.

An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.