Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.

Compression coding may be used with forward error correction (FEC) coding to provide higher information rates by reducing the proportion of redundant bits relative to information bits that are transmitted from a transmitter to a receiver. In one example, first determiners and second determiners are calculated from a set of information bits, where each first determiner is calculated from a different first subset of the information bits along a first dimension, and each second determiner is calculated from a different second subset of the information bits along a second dimension that differs from the first dimension. First and second nubs are calculated from the first and second determiners, respectively, each nub comprising a number of redundant bits that is less than the number of bits in the determiners from which the nub is calculated. The information bits and the nubs are transmitted over one or more communications channels.

Technologies for error recovery in compressed data streams include a compute device configured to compress uncompressed data of an input stream to generate compressed data, perform a compression error check on the compressed data to verify integrity of the compressed data, and determine, as a result of the performed compression error check, whether the compressed data included a compression error. The compute device is further configured to transfer, in response to a determination that the performed compression error check indicated that the compressed data included the compression error, the uncompressed data into a destination buffer, and store an indication with the uncompressed data into the destination buffer, wherein the indication is usable to identify that the uncompressed data has been transferred into the destination buffer. Other embodiments are described herein.

A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.

A method of near-lossless universal data compression using correlated data sequences includes detecting first target surroundings via a first sensor, encoding a first data sequence indicative of the detected target surroundings, and communicating to an electronic controller, the encoded first data sequence. The method additionally includes detecting the first target surroundings via a second sensor, and encoding a second data sequence indicative of the target surroundings detected by the second sensor. The method also includes communicating the encoded second data sequence to the controller. The method additionally includes decoding, via the controller, the encoded first and second data sequences. The method also includes, via the controller, determining a statistical correlation between the decoded first and second data sequences and formulating a mapping function having reduced cardinality and indicative of the determined statistical correlation. Furthermore, the method includes feeding back the mapping function by the controller to the first processor.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

A storage device may include a decoder configured to connect bits to a content node based on content-aware decoding process. The content-aware decoding process may be dynamic and determine connection structures of bits and content nodes based on patterns in data. In some cases, the decoder may connect non-adjacent bits to a content node based on a content-aware decoding process. In other cases, the decoder may connect a first number of bits to a first content node and a second number of bits to a second content node. In such cases, the first number of bits and the second number of bits are a different number.

A method and apparatus for obtaining data from a memory, estimating a probability of data values of the obtained data based on at least one of a source log-likelihood ratio and a channel log-likelihood ratio, wherein each bit in the obtained data has an associated log-likelihood ratio, determining at least one data pattern parameter for the data and performing a decoding process using the at least one data pattern parameters to determine a decoded data set.