Abstract: A data storage device generates a recommended read threshold value by combining outputs of a binary tree model and a linear regression model. This provide a best-of-both-worlds result, as a binary tree model can describe complex output functions but can be limited to a finite set of output values, whereas the linear regression model has continuous value outputs. Other embodiments are provided.

Abstract: Metadata that is generated during the life of machine learning and artificial intelligence systems are valuable. However, such metadata may be generated subsequent to the time of the data generation, and thus written to the storage device later. By further supporting KV databases on the storage device level, performance may be increased in terms of transfers per second. This is due to the removal of the translation layer in the host, which was previously required for data storage. The removal of the translation layer provides for the removal of two layers of mapping and transaction information. As a result, the number of transactions per second, write amplification, and read amplification increase, while latency decreases. Additionally, future additions of metadata are considered by reserving excess memory at the time of storing the start key value. The future additions are then saved to the reserved memory later.

Abstract: A data storage device generates a recommended read threshold value by combining outputs of a binary tree model and a linear regression model. This provide a best-of-both-worlds result, as a binary tree model can describe complex output functions but can be limited to a finite set of output values, whereas the linear regression model has continuous value outputs. Other embodiments are provided.

Abstract: A data storage device includes an error correction code (ECC) system and a decoder gear determination system. The decoder gear determination system dynamically enhances error correction capabilities of a decoder of the ECC system should the decoder fail to decode a codeword. The decoder gear determination system enhances the error correction capabilities of the decoder prior to initiating a gear switch, in which another decoder, with higher error correction capabilities, is used to decode the codeword. The decoder gear determination system enhances the error correction capabilities of the decoder by deriving information about the failed decoding attempt. The derived information is used to generate an updated decoding parameter that is provided to the decoder. The decoder attempts a subsequent decoding process on the codeword using the updated decoding parameter.

Abstract: Key value (KV) pair data includes a key and a value, where the key addresses the value. The value may include one or more flash management units (FMUs). Because the value is read in order, sequentially from one FMU to a next FMU, end-to-end (E2E) protection of the value may be optimized and improved. E2E protection may including using checksum signatures, of which cyclic redundancy code (CRC) signatures are but one example, to ensure that corrupted data is not returned to a host device. Optimizing checksum signatures used to protect the value may include generating an aggregated checksum signature for each FMU based on a current FMU and each previous FMU of the value or only generating a single checksum signature for an entirety of the value. Thus, characteristics of the value may be taken advantage of in order to improve and optimize E2E protection.

Abstract: Data privacy and fulfilling security limitations are ensured during ML algorithm and AI model training by forcing the distinct separation of stored data of each data storage device and preventing the allowance of information sharing between other data storage devices. Specifically, a privacy-preserving information-sharing method is implemented between data storage devices in a joint system. The data of each storage device is not exposed to other storage devices in the joint system. Instead, predictive conclusions based on statistical and ML analysis derived from the collective data of all the storage devices is observed by each storage device. Thus, by allowing the sharing of data insights between storage devices without exposing the data of each storage device to other storage devices, performance and reliability of a storage device is improved.

Abstract: A model can be used to infer a read threshold for reading a memory of a data storage device. In some situations, such as when the memory has an open wordline or an open block, the model may not provide an accurate read threshold. In such situations, one or more additional models can be used as modular add-ons to the original model to provide a more-accurate read threshold, which can result in a reduced bit error rate, as well as improved throughput, quality of service, and power consumption.

Abstract: Technology is disclosed herein for memory health monitoring and mitigation based on decoding statistics. Decoding a frame results in a decoding metric (syndrome weight, fail bit count) for that frame. The system tracks a statistic for different sets of frames. The statistic for a set is based on the decoding metrics for that set. The frames may be assigned to sets based on read reference voltages used to read frames or the physical location of the memory cells that store the frames. Memory health mitigation may be performed based on the decoding statistics. One example mitigation is to modify the read reference voltages for the set. Another example mitigation is to trigger reading at soft bit reference levels for a block. Another example mitigation is to trigger direct look ahead reading for a block. Still another example mitigation is to add a block to list of candidates for data refresh.

Abstract: A data storage device can generate a recommended read threshold value using a model that was trained under a plurality of conditions. However, such a model may result in an undesirable bit error rate or programming latency. If that should occur, the data storage device can use a different model trained under a condition similar to a current condition of the data storage device. In addition to avoiding an undesirable bit error rate or programming latency, this can result in improved throughput, improved quality of service, and reduced power consumption.

Abstract: A predictive read threshold calibration method is provided in which a dedicated hardware module is used for read threshold calibration. The hardware module uses a tree-based inferencing model to generate trees that produce respective inference results based on a plurality of read thresholds. An inferred read threshold is obtained by summing the inference results of the trees, and the memory is read using the inferred read threshold. Other embodiments are provided.

Abstract: A data storage device has an inference engine that can infer a read threshold based on a non-linear function of inputs that reflect current memory and data conditions. The read threshold can be used in reading a wordline in the memory. Using a machine-learning-based approach to infer a read threshold can provide significant improvement in read threshold accuracy, which can reduce bit error rate and also improve latency, throughput, power consumption, and quality of service.

Abstract: A state transition matrix for a data storage system indicates a reliability of data that was read or decoded from a data storage channel. The state transition matrix includes a probability of reading each state of a nucleotide base in an identified storage material when that nucleotide base was initially programmed in a particular state. The state transition matrix is generated by identifying a storage material with the most copies in the data storage system. During a sequencing process, the identified storage material is decoded, corrected and compared against the storage material that was originally synthesized. This information is used to determine the probability values for the state transition matrix. The state transition matrix is provided to an error correction system of the data storage system, which uses the probability information when determining whether decoded data of other storage materials should be corrected.

Abstract: Before a read threshold is needed to read a wordline in memory, a data storage device can infer a plurality of read thresholds based on possible conditions of the memory that may exist when the read threshold is eventually needed. When the read threshold is needed, it is selected from the previously-inferred read thresholds based on the current conditions of the memory. This can improve latency and throughput, improve quality of service, reduce power consumption, and reduce errors.

Abstract: A storage system has an inference engine that can infer a read threshold based on a plurality of parameters of the memory. The read threshold can be used in reading a wordline in the memory during a regular read operation or as part of an error handling process. Using a machine-learning-based approach to infer a read threshold can provide significant improvement in read threshold accuracy, which can reduce bit error rate and improve latency, throughput, power consumption, and quality of service. In another embodiment, a circuit-bounded array is used to manage updates to time and temperature tag information and to infer read thresholds.

Abstract: A DNA-based storage system includes an error correction system operable to: (a) identify a DNA codeword from a DNA sequencing operation; (b) calculate an initial syndrome weight; (c) determine that the initial syndrome weight is greater than a predetermined threshold; (d) perform an alignment alteration in the information segment by: (i) selecting a skew point within the information segment; (ii) performing an indel operation on the information segment at the skew point; (iii) calculating a modified syndrome weight; (iv) comparing the initial syndrome weight with the modified syndrome weight; and (v) incorporating the indel operation into the information segment when the comparing indicates an improvement in the modified syndrome weight; (e) decode the modified codeword; and (f) transmit the contents of the output file to a computing device, the output file representing user data stored within the DNA molecule.

Abstract: Storage devices are configured to be utilized in a variety of blockchain related activities that rely on a proof of space consensus model. These storage devices are required to process a lot of read and write cycles on their memory devices to generate the desired proof of space consensus data. The generation and storing of this generated data requires very different types of memory device usage. Storage devices may be configured with a first partition for high-speed access for generating the data, while a second partition is also configured for long-term storage of the generated data. As memory devices reach their estimated end-of-life, they can be dynamically reassigned to the second partition. Likewise, some storage devices may be equipped with multiple memory arrays of different types of memory devices. One set of memory devices can be used for generation, while cheaper, write-once or few memory devices are provided for storage.

Abstract: A DNA-based storage system implements a sub-code architecture for error correction capability (ECC) purposes. The sub-code architecture enables a long DNA strand to be divided into two or more short DNA strands. Each short DNA strand has its own unique parity information. Additionally, each short DNA strand is separately decodable from the other short DNA strands. The parity information associated with a particular short DNA strand is used to correct any errors that occur or are detected during the decoding process. However, if the decoding and error correction processes are not successful using the parity information associated with the particular short DNA strand, global parity information is used to decode the particular short DNA strand and correct the errors. Global parity information includes information from each short DNA strand and the parity information associated with each short DNA strand.

Abstract: A data storage device includes an error correction code (ECC) system and a decoder gear determination system. The decoder gear determination system dynamically enhances error correction capabilities of a decoder of the ECC system should the decoder fail to decode a codeword. The decoder gear determination system enhances the error correction capabilities of the decoder prior to initiating a gear switch, in which another decoder, with higher error correction capabilities, is used to decode the codeword. The decoder gear determination system enhances the error correction capabilities of the decoder by deriving information about the failed decoding attempt. The derived information is used to generate an updated decoding parameter that is provided to the decoder. The decoder attempts a subsequent decoding process on the codeword using the updated decoding parameter.

Abstract: A DNA-based storage system stores DNA strands that are divided into N tiers. Due to the nature of the DNA storage channel, certain types of errors occur less frequently toward the front of a DNA strand and occur more frequently near the end of the DNA strand. As such, each tier stores data having a different level of importance when compared with other types of data stored in the different tiers. Because data stored near the front of the DNA strand has fewer errors than data stored near the end of the DNA strand, the data stored near the front of the DNA strand is decoded more quickly when compared with data near the end of the DNA strand.

Abstract: A DNA-based storage system uses a set of initial state transition probabilities during a decoding process in which a DNA codeword is decoded. After a threshold number of decoding iterations of the decoding process have been executed, at least one initial state transition probability of the set of initial state transition probabilities is updated. The at least one initial state transition probability is updated based, at least in part, on information obtained during the threshold number of decoding iterations. When the at least one initial state transition probability is updated, the decoding process resumes and uses the updated at least one initial state transition probability.