Abstract: A data storage device and method for enhanced recovery through data storage device discrete-component-hardware-reset are provided. In one embodiment, the data storage device determines that a subset of a plurality of memory dies is non-responsive, sends a request to a host to accept longer delays associated with the subset of the plurality of memory dies, power-cycles the subset of the plurality of memory dies, and then informs the host that the latency associated with those dies has been restored to normal latency or that the subset of the plurality of memory dies are inactive (in case of unsuccessful recovery). Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

Abstract: A data storage device includes a memory device, a random access memory (RAM) device, and a controller coupled to the memory device and the RAM device. The controller is configured to determine a workload type of the data storage device, determine to store at least a portion of a compressed logical to physical translation table (ZCAT) in the RAM device based on the workload type, and utilize a remaining portion of the RAM device to perform background operations. The controller is further configured to determine a persistence or temperature of the ZCAT, where the portion of the ZCAT stored to the RAM device is based on the persistence and/or the temperature of the ZCAT. The remaining portion of the ZCAT is stored in the HMB, where the remaining portion may update any out-of-date ZCAT entries.

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: Data storage devices configured to exploit generative-adversarial-networks (GANs). The GANs include super-resolution GANs (SRGANS). In some examples, a GAN-based decoding reconstruction procedure is implemented within a data storage controller to replace or supplement an error correction coding (ECC) decoding procedure. In other examples, soft bit information is exploited using GANs during decoding. A dissimilarity matrix may be generated to represent differences between an initial image and a GAN-reconstructed image, with matrix values mapped into low-density parity check (LDPC) codewords to facilitate LDPC decoding of data. In still other examples, confidence information obtained from a GAN is incorporated into image pixels. In some examples, GAN reconstruction of data is limited to modifying valley bits. Multiple GANs may be used in parallel with their outcome aggregated.

Abstract: The present disclosure generally relates to utilizing improved DL training models stored in non-volatile memory to optimize data transfer and storage. The proposed system would identify workloads of DNN training and occasionally check the difference rate between successive data transfers (representing successive training iterations of the model). Comparing the difference rate to given thresholds could indicate “recommendation-system” typical use case. In such a case the NAND operating system would apply systematic compression of the data by saving only the changed parameters between successive iteration cycles (“batches”). The host may indicate the checkpoint storage configuration of the training model (every iteration, every several iterations etc. . . . ) and other elements. The system may be efficiently utilized combining the NAND based DNN training interface, adding the checkpoint configuration information to the dedicated interface.

Abstract: A data storage device and method for dynamic controller memory buffer allocation are disclosed. In one embodiment, a data storage device is provided comprising a memory and a controller with a controller memory buffer. The controller is configured to communicate with the non-volatile memory and is further configured to configure a size of the controller memory buffer; receive a request from the host to modify the size of the controller memory buffer during operation of the data storage device; and determine whether to grant the request to modify the size of the controller memory buffer. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

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: Rather than using low power mode since there is no indication of a sleep mode type to the data storage device, the sleep mode type is inferred by the data storage device, or supplied by the host. In so doing, the sleep type communication is improved. After the system returns power to the data storage device, there may be a read workload, depending on the sleep type. The workload is characterized by a low-queue depth (QD) sequential read from a specific area of the storage medium that was written to just prior to shut down. In response to inference or host cue, the data storage device will modify an operation so that data storage device is optimized to the sleep mode type, resulting in improved performance and power consumption.

Abstract: More efficient memory device usage is possible by altering the memory device management. For example, when the full storage capacity of the memory device will not be used, certain portions of the memory device can be shut off and then turned on when the storage capacity is needed. When less capacity is needed, data can be consolidated and certain portions of the memory device can be shut off. Additionally, rather than operating in multilevel cell (MLC) memory, the memory device can start in single level cell (SLC) memory and transition to MLC memory over time. If there is a determination that less memory is needed, the memory device can transition from MLC memory to SLC memory. In so doing, the storage capacity of the memory device is more appropriately utilized.

Abstract: In order to guarantee data validity of data read from a memory device of the data storage device to a host device, a controller of the data storage device may calculate a cyclic redundancy code (CRC) signature of the decoded data and compare the CRC signature of the decoded data with a CRC signature of the data. The CRC signature of the data is generated during a write operation of the data to the memory device. Rather than returning an uncorrectable error correction code error (UECC) error to the host device when the CRC signature of the decoded data does not match the CRC signature of the data, the controller executes the read command again. By using a different buffer to store the decoded data, the controller may confirm whether the error stemmed from the read path or the error was not from the read path.

Abstract: During data storage device operation, data of multiple blocks of a non-volatile memory device, logically grouped as a jumboblock, may be protected by an exclusive or (XOR) signature, where the XOR signature may be used to recover data of a block of the multiple blocks. During a recovery/relocation operation, data of the jumboblock is read from the non-volatile memory device during the recovery of the lost data and again when the data is relocated. However, because the data read during data storage device operation is temporarily stored in a volatile memory device, the controller utilizes the relevant data stored in the volatile memory device and the data stored in the non-volatile memory device to recover corrupted data. Thus, the amount of reads from the non-volatile memory device decreases due to the relevant data is read from the volatile memory device, which may improve data storage device performance.

Abstract: The present disclosure generally relates to aligning non-aligned data for more efficient data reading. Data for write commands does not always perfectly align, yet the data is written in order of write command receipt. In such cases, aligned chunks of data may be split into two word lines (WLs) due to the presence of previously received smaller chunks of data. Rather than writing the data in order, the smaller chunks of data, which are non-aligned, are held in a buffer and written later to ensure that any aligned chunks of data remain aligned when written to the memory device. Once sufficient smaller chunks or data have accumulated to be aligned, or upon a need to write the smaller chunks upon reaching a threshold, the smaller chunks are written together in a single WL so as to not cause non-alignment of aligned data.

Abstract: Systems, methods, and data storage devices for secured failover access through data storage device side channels are described. Storage devices may include a storage interface and one or more side channels, such as control bus and debug bus interfaces. The different interfaces may use different interface protocols and physical interface connections configured for different types of commands to the data storage device. When a failure of the storage interface occurs, the data storage device may receive a failover message through one or more side channels to reconfigure the side channel interface to receive, execute, and return a response for host storage commands targeting host data on the storage medium.

Abstract: In a storage system having a plurality of solid state drives (SSDs), the performance of propagating data from a primary device to each secondary device may be improved using a dedicated high speed data channel in which data and commands associated with the data is sent from an upstream SSD to a downstream SSD. The data is also sent to the downstream SSD after a minimum amount of data has been programmed to the upstream SSD. The downstream SSD begins programming the data to its own memory device after receiving the data. The programming of data to each SSD of the storage system may be in parallel and at least partially concurrent with each other. Data, commands, and control messages may be sent an upstream SSD via a serial bus or a universal asynchronous receiver-transmitter channel, such that the downstream data paths and the upstream data paths are distinct.

Abstract: A data storage device with partial speed changes to improve in-order data transfer. Rather than determining an ECC decoder on a first available decoder basis, the ECC decoder may be based on the ECC decoder level. A memory device will have at least one FMU that has a syndrome weight (SW). The disclosure proposes assigning FMU's based on the SW rate. At the time the command is read, the data storage device determines which level of decoder will be assigned to the FMU. The determination will then be checked according to different system environment parameters to maintain performance or reduce power consumption. The arrangement allows a more flexible system design that can adapt according to the current system status.

Abstract: Optimizing the time that a link is active in a data storage device is desirable. Changing the way the device processes commands will minimize the link uptime and maximize the time that the link can remain in a low power mode. The data storage device will control the command arbitration from the host to aggregate together command chunks as large as possible, such that will extend the link down durations, and won't need to wake the link up occasionally. In another approach the execution of commands from internal buffers of the host will be prioritized according to command-batch completion criteria, and not based on minimizing the latency of a single command.

Abstract: A controller memory buffer (CMB) is a portion of volatile memory of a controller of a data storage device that is allocated to a host device for use by the host device. When the CMB is not fully utilized, the controller may determine that at least a portion of the unutilized space of the CMB may be used for non-host data. The at least a portion is based on a number of past workloads and a current workload of the CMB. An amount of available space of the CMB that the controller may utilize is dependent on the number of past workloads and the current workload of the CMB. Thus, the volatile memory of the controller may be more optimally utilized.

Abstract: Detecting the removal of a data storage device from a storage system involves first determining that a shorter pin of an electrical connector of a storage device is disconnected from a mating electrical connector, such as by sensing a voltage drop on that pin, then determining at a later time that a longer pin of the connector is disconnected from the mating connector. Responsive to determining that the longer pin was disconnected after a predetermined period of time after the shorter pin, a conclusion may be made that the storage device has been removed from the system as opposed to being subject to a simple device power aberration. Thus, responsive data destruction action(s) may be taken to render the data stored on the device inaccessible to the attacker thereby protecting the device even after the device is removed from the storage system.

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 require very different types of memory device usage. As these operations increase in popularity, the need to segment storage devices meant for proof of space usage and those which are not becomes more important. Storage devices may be configured to throttle these different usage types upon detecting these proof of space blockchain activities. Throttling may include reducing clock frequencies, selecting slower performing trim parameters, and programming memory devices with a reduced voltage window, among other processes. Detecting whether throttling should commence, or end can be done via a deployed machine learning classifier.

Abstract: Data storage devices configured to exploit generative-adversarial-networks (GANs) are described herein, including super-resolution GANs (SRGANs). In some examples, a GAN-based decoding (reconstruction) procedure is implemented within a data storage controller to replace or supplement an error correction coding (ECC) decoding procedure to permit a reduction in the number of parity bits used while storing the data. In other examples, soft bit information is exploited using GANs during decoding. A dissimilarity matrix may be generated to represent differences between an initial image and a GAN-reconstructed image, with matrix values mapped into low-density parity check (LDPC) codewords to facilitate LDPC decoding of data. In still other examples, confidence information obtained from a GAN is incorporated into image pixels. In some examples, GAN reconstruction of data is limited to modifying valley bits. Multiple GANs may be used in parallel with their outcome aggregated. System and method examples are provided.