Abstract: A Redundant Array of Independent Disks (RAID) scheme can be used to allow recovery from a memory failure. RAID 6 can be used to recover data from up to two catastrophic failures. Typically, a hardware encoder is used to generate parities of the RAID 6 protection scheme, and a separate hardware decoder is used in the data recovery process. In an example data storage device described herein, some of the components of the hardware encoder are used to perform the data recovery process, thereby eliminating the need for a separate, additional hardware component.

Abstract: A storage device may speed up error correction by pre-characterizing weak cell information in a memory device. The storage device includes a memory device with cells that may store multiple bits. A controller executes a pre-characterization operation on the memory device to identify a slow cell and/or a fast cell on the memory device. The controller retrieves weak cell information for the slow cell and/or the fast cell. The controller converts the weak cell information into values used by an error correction engine and provides the values to the error correction engine to be used in decoding information retrieved from the memory device.

Abstract: A Redundant Array of Independent Disks (RAID) scheme can be used to allow recovery from a memory failure. RAID 6 can be used to recover data from up to two catastrophic failures. Typically, a hardware encoder is used to generate parities of the RAID 6 protection scheme, and a separate hardware decoder is used in the data recovery process. In an example data storage device described herein, some of the components of the hardware encoder are used to perform the data recovery process, thereby eliminating the need for a separate, additional hardware component.

Abstract: A storage device may speed up error correction by pre-characterizing weak cell information in a memory device. The storage device includes a memory device with cells that may store multiple bits. A controller executes a pre-characterization operation on the memory device to identify a slow cell and/or a fast cell on the memory device. The controller retrieves weak cell information for the slow cell and/or the fast cell. The controller converts the weak cell information into values used by an error correction engine and provides the values to the error correction engine to be used in decoding information retrieved from the memory device.

Abstract: Systems and methods for offloading data storage processing tasks from a data storage device to a graphics processing unit data are described. Data storage devices may include a peripheral interface configured to connect to a host system and provide access to a host memory buffer. The data storage device may store task input data to the host memory buffer. The data storage device may notify a processor device including the graphics processing unit to initiate the storage processing task. The processor device may access the task input data from the host memory buffer and store the task output data to the host memory buffer for access by the data storage device.

Abstract: Systems and methods for offloading data storage processing tasks from a data storage device to a graphics processing unit data are described. Data storage devices may include a peripheral interface configured to connect to a host system and provide access to a host memory buffer. The data storage device may store task input data to the host memory buffer. The data storage device may notify a processor device including the graphics processing unit to initiate the storage processing task. The processor device may access the task input data from the host memory buffer and store the task output data to the host memory buffer for access by the data storage device.

Abstract: A storage system generates a low-density parity check (LDPC) code from a plurality of subcodes. The storage system stores each subcode in a different page of a word line in the memory. The subcode can be stored in one plane in the memory or across multiple planes. When the subcodes are stored across multiple planes, they can be stored in a checkboard pattern.

Abstract: A storage system generates a low-density parity check (LDPC) code from a plurality of subcodes. The storage system stores each subcode in a different page of a word line in the memory. The subcode can be stored in one plane in the memory or across multiple planes. When the subcodes are stored across multiple planes, they can be stored in a checkboard pattern.

Abstract: Calibration of soft bit reference levels in a non-volatile memory system is disclosed. A set of memory cells are sensed at a hard bit reference level and test soft bit reference levels. The test soft bit reference levels are grouped around the hard bit reference level. A metric is determined for the test soft bit reference levels. Bins are defined based on the hard bit reference level and the set of test soft bit reference levels. A metric may be determined for each of the bins. The new soft bit reference levels are determined based on the metric. In one aspect, the metric is how many memory cells have a value for a physical parameter within each bin. The soft bit reference levels may be established based on a target percentage for the bins. In one aspect, the metric is how many unsatisfied counters are within each bin.

Abstract: Calibration of soft bit reference levels in a non-volatile memory system is disclosed. A set of memory cells are sensed at a hard bit reference level and test soft bit reference levels. The test soft bit reference levels are grouped around the hard bit reference level. A metric is determined for the test soft bit reference levels. Bins are defined based on the hard bit reference level and the set of test soft bit reference levels. A metric may be determined for each of the bins. The new soft bit reference levels are determined based on the metric. In one aspect, the metric is how many memory cells have a value for a physical parameter within each bin. The soft bit reference levels may be established based on a target percentage for the bins. In one aspect, the metric is how many unsatisfied counters are within each bin.

Abstract: An error correcting code (ECC) decoder for a non-volatile memory device is configured to decode data stored by the non-volatile memory device using a parity check matrix with columns of different column weights. The ECC decoder is further configured to artificially slow processing of one or more of the columns of the parity check matrix in response to column weights for the one or more columns satisfying a threshold.

Abstract: A method for punctured bit estimation includes receiving a punctured codeword. The method further includes generating a reconstructed codeword using the punctured codeword and at least one punctured bit having a default logic value. The method further includes generating a syndrome vector for the reconstructed codeword. The method further includes determining, using the syndrome vector, a number of unsatisfied parity-checks for the at least one punctured bit. The method further includes determining, for the at least one punctured bit, a bit value using, at least, the number of unsatisfied parity-checks associated with the at least one punctured bit.

Abstract: A method for punctured bit estimation includes receiving a punctured codeword. The method further includes generating a reconstructed codeword using the punctured codeword and at least one punctured bit having a default logic value. The method further includes generating a syndrome vector for the reconstructed codeword. The method further includes determining, using the syndrome vector, a number of unsatisfied parity-checks for the at least one punctured bit. The method further includes determining, for the at least one punctured bit, a bit value using, at least, the number of unsatisfied parity-checks associated with the at least one punctured bit.

Abstract: In an illustrative example, a method includes sensing at least a portion of a representation of a convolutional low-density parity-check (CLDPC) codeword stored at a memory of a data storage device. The method further includes receiving the portion of the representation of the CLDPC codeword at a controller of the data storage device. The method further includes performing one or more management operations associated with the memory based on an estimated number of errors of the portion of the representation of the CLDPC codeword.

Abstract: In an illustrative example, an apparatus includes a controller and a memory that is configured to store a codeword of a convolutional low-density parity-check (CLDPC) code. The codeword has a first size and includes multiple portions that are independently decodable and that have a second size. The controller includes a CLDPC encoder configured to encode the codeword and a CLDPC decoder configured to decode the codeword or a portion of the codeword.

Abstract: A black box recorder for an autonomous vehicle includes an interface configured to receive data from an engine control unit (ECU) device. The data includes first data and second data. The black box recorder further includes an error correction code (ECC) engine configured to determine a first parity size associated with the first data based on a characteristic of the first data and a second parity size associated with the second data based on a characteristic of the second data. The first parity size is different than the second parity size. The ECC engine is further configured to generate a convolutional low-density parity-check (CLDPC) codeword that includes the first data, the second data, first redundancy data associated with the first data, and second redundancy data associated with the second data. The first redundancy data has the first parity size, and the second redundancy data has the second parity size.

Abstract: In an illustrative example, a method includes receiving data to be processed in accordance with a convolutional low-density parity-check (CLDPC) code. The method also includes processing the data based on a parity check matrix associated with the CLDPC code. The parity check matrix includes a first portion and a second portion. The first portion includes a plurality of copies of a first sub-matrix that is associated with a first sub-code, and the second portion includes a copy of second sub-matrix that is associated with a second sub-code.

Abstract: A storage device may program data differently for different memory areas of a memory. In some embodiments, the storage device may use different codebooks for different memory areas. In other embodiments, the storage device may modify bit orders differently for different memory areas. What codebook the storage device uses or what bit order modification the storage device performs for a particular memory area may depend on the bad storage locations specific to that memory area. Where different codebooks are used, optimal codebooks may be selected from a library, or codebooks may be modified based on the bad storage locations of the memory areas.

Abstract: A black box recorder for an autonomous vehicle includes an interface configured to receive data from an engine control unit (ECU) device. The data includes first data and second data. The black box recorder further includes an error correction code (ECC) engine configured to determine a first parity size associated with the first data based on a characteristic of the first data and a second parity size associated with the second data based on a characteristic of the second data. The first parity size is different than the second parity size. The ECC engine is further configured to generate a convolutional low-density parity-check (CLDPC) codeword that includes the first data, the second data, first redundancy data associated with the first data, and second redundancy data associated with the second data. The first redundancy data has the first parity size, and the second redundancy data has the second parity size.

Abstract: Systems and methods are described for generating location-based read voltage offsets in a data storage device. Optimal read voltage thresholds vary across memory elements of a device. However, data storage devices are often limited in the number of read voltage thresholds that can be maintained in the device. Thus, it may not be possible to maintain optimal read voltage parameters for each memory element within a device. The systems and methods described herein provide for increased accuracy of read voltage thresholds when applied to memory elements within a specific location in a device, by enabling the use of location-based read voltage offsets, depending on a relative location of the memory element being read from. The read voltage offsets can be determined based on application of a neural network to data regarding optimal read voltage thresholds determined from at least a sample of memory elements in a device.