Abstract: Disclosed herein are memory devices, systems, and methods of encoding and decoding data. In one aspect, an encoded data chunk is received and segmented into data segments with similar features. Each segment can be decoded based on its features. Data can also be rearranged and partitioned so as to minimize an entropy score that is based on the size and entropy of the data partitions. The approach is capable of enhancing performance, reducing decoding latency, and reducing power consumption.

Abstract: A method for improving decoding and error correction in non-volatile memory, e.g., solid state drives. A map is generated for each data type that has a structure, e.g., text files. The map can be based on the underlying characteristics of this type of the data file and the mutual information between Lout to a soft decoder. The map transforms the data prior to encoding to condition the data to improve decoding and error correction.

Abstract: Apparatuses, systems, and methods are disclosed for dynamic read operations. A controller monitors one or more read statistics during a read operation for data of a non-volatile memory die. The controller determines whether one or more read statistics satisfy a threshold for a read operation. The controller dynamically modifies a read operation based on determining that one or more read statistics fail to satisfy a threshold.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are memory devices, systems, and methods of encoding and decoding data. In one aspect, an encoded data chunk is received and segmented into data segments with similar features. Each segment can be decoded based on its features. Data can also be rearranged and partitioned so as to minimize an entropy score that is based on the size and entropy of the data partitions. The approach is capable of enhancing performance, reducing decoding latency, and reducing power consumption.

Abstract: A method for improving decoding and error correction in non-volatile memory, e.g., solid state drives. A map is generated for each data type that has a structure, e.g., text files. The map can be based on the underlying characteristics of this type of the data file and the mutual information between Lout to a soft decoder. The map transforms the data prior to encoding to condition the data to improve decoding and error correction.

Abstract: 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.

Abstract: Disclosed herein is a memory device and a method of descrambling and decoding encoded data. In one aspect, encoded data is received. A scrambling seed is obtained from the encoded data prior to decoding the encoded data. The encoded data is descrambled according to the scrambling seed, and the descrambled data is decoded. The descrambled data may be decoded according to statistics of the descrambled data.

Abstract: 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.

Abstract: Disclosed herein are memory devices, systems, and methods of content-aware decoding of encoded data. In one aspect, an encoded data chunk is received and one or more characteristics, such as source statistics, are determined. A similar data chunk (that may, e.g., contain data of a similar type) with comparable statistics may be sought. The similar data chunk may, for example, have source statistics that are positively correlated to the source statistics of the encoded data chunk to be decoded. Decoder parameters for the encoded data may be set to correspond with decoder parameters suited to the similar data chunk. The encoded data chunk is decoded using the new decoder parameters. Decoding encoded data based on content can enhance performance, reducing decoding latency and/or power consumption.

Abstract: Apparatuses, systems, and methods are disclosed for dynamic read operations. A controller monitors one or more read statistics during a read operation for data of a non-volatile memory die. The controller determines whether one or more read statistics satisfy a threshold for a read operation. The controller dynamically modifies a read operation based on determining that one or more read statistics fail to satisfy a threshold.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for seed generation. An apparatus includes a memory element. An apparatus includes a scrambler component. A scrambler component includes a random seed generation circuit that generates a random seed. A scrambler component includes a deterministic seed generation circuit that generates a deterministic seed based on a physical address of a memory element for storing data. A scrambler component includes a computation circuit that forms a computed seed based on a random seed and a deterministic seed. Data is scrambled using a computed seed before data is stored.

Abstract: A memory system configured to decode a data set may pause a convergence process to update reliability metric values. The memory system may utilize a positive feedback system that updates the reliability metric values by analyzing current a posteriori reliability metric values to calculate average estimated reliability characteristic values associated with a memory error model. The updates to the reliability metric values may provide increased error correction capability and faster decoding.

Abstract: 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.

Abstract: 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.

Abstract: A memory system configured to decode a data set may pause a convergence process to update reliability metric values. The memory system may utilize a positive feedback system that updates the reliability metric values by analyzing current a posteriori reliability metric values to calculate average estimated reliability characteristic values associated with a memory error model. The updates to the reliability metric values may provide increased error correction capability and faster decoding.

Abstract: Disclosed herein is a memory device and a method of descrambling and decoding encoded data. In one aspect, encoded data is received. A scrambling seed is obtained from the encoded data prior to decoding the encoded data. The encoded data is descrambled according to the scrambling seed, and the descrambled data is decoded. The descrambled data may be decoded according to statistics of the descrambled data.

Abstract: A non-volatile storage apparatus includes a set of non-volatile memory cells, one or more control circuits in communication with the set of non-volatile memory cells, the one or more control circuits are configured to encode data with a code rate prior to storage in the set of non-volatile memory cells, the code rate selected from two or more code rates according to one or more predictive indicators received with the data, the one or more predictive indicators relating to expected conditions for storage of the data in the set of non-volatile memory cells.

Abstract: Technology is described herein for learning parameters for a parameterized iterative message passing decoder, and to a corresponding parameterized iterative message passing decoder. Learning the parameters may adapt the decoder to statistical dependencies introduced by the specific code's graph. Taking into account the statistical dependencies may allow the code to be shorter and/or denser. Note that the statistical dependencies in the graph may be extremely complex. Machine learning may be used to learn the parameters. The parameters may be learned when decoding data stored in the memory device. Learning the parameters may adapt the decoder to properties of data stored in the memory device, physical properties of the memory device, and/or patterns in host data.