Abstract: A processing device, operatively coupled with a memory device, determines a current workload characteristic of the memory device. The processing device further determines, by a trainable classifier processing the current workload characteristic, a first set of one or more parameter values that satisfies a threshold workload criterion associated with the memory device. The processing device further configures the firmware component of the memory device with the first set of one or more parameter values.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including sending, to a device, log data comprising a first order of a set of error-handling operations to be performed on data residing in a segment of the memory device; receiving, from the device, a second order of the set of error-handling operations, wherein the second order is obtained by optimizing the log data, based on probability data of error recovery and latency data of the set of error-handling operations; and performing one or more error-handling operations of the set of error-handling operations in the second order on the data residing in the segment of the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including obtaining a reordered set of error-handling operations, wherein the reordered set adjusts an order of one or more error-handling operations of an ordered set of error-handling operations to be performed to recover data residing in a segment of the memory device based on latency data for previously-performed error-handling operations, wherein the latency data for the previously-performed error-handling operations depends on a workload of the segment of the memory device; and performing one or more error-handling operations of the reordered set of error-handling operations on the data residing in the segment of the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including selecting sample data residing in the memory device; running a test on the sample data regarding a set of error-handling operations; and generating log data comprising a first order of the set of error-handling operations to be performed on data residing in a segment of the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including providing current workload data of the memory device as input to a machine learning model, wherein the machine learning model is trained, using a plurality of workload data, to identify one or more power-saving parameters associated with each workload data of the plurality of workload data. The processing device can perform operations further including obtaining an output of the machine learning model, the output comprising the one or more power-saving parameters associated with the current workload data. The processing device can perform operations further including adjusting, based on the one or more power-saving parameters associated with the current workload data, a power state of the memory device.

Abstract: Systems and methods are disclosed including a memory and a processing device operatively coupled to the memory. The processing device can perform operations including identifying a set of logical addresses associated with data stored on the memory devices in one or more blocks of a first type; determining a temporal metric class associated with the set of logical addresses, wherein the temporal metric class is associated with a corresponding range of predicted update characteristic of the data; identifying, based on the temporal metric class, a set of blocks of a second type, wherein a first block of the first type comprises a first plurality of memory cells having a first number of bits per cell, and wherein a second block of the second type comprises a second plurality of memory cells having a second number of bits per cell that exceeds the first number of bits per cell; and moving the data to the identified set of blocks.

Abstract: A processing device, operatively coupled with a memory device, determines a current workload characteristic of the memory device. The processing device further determines, by a trainable classifier processing the current workload characteristic, a first set of one or more parameter values that satisfies a threshold workload criterion associated with the memory device. The processing device further configures the firmware component of the memory device with the first set of one or more parameter values.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: An interchangeable custom covering system (ICCS) for personal items such as caps, backpacks, handbags, watches, footwear, belts and cell phone cases etc. is disclosed. The system is comprised of a transparent, custom-fitted, flexible window that affixes to multiple articles and accessories to display personalized messages and graphics. For example, a user can obtain the customized window, create a team logo, print it out on a card and insert said card into the window affixed to a product such as a baseball cap. Other embodiments can include said cards having a multitude of shapes, sizes, colors, patterns and text thereon that significantly change the appearance of a product.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including selecting sample data residing in the memory device; running a test on the sample data regarding a set of error-handling operations; and generating log data comprising a first order of the set of error-handling operations to be performed on data residing in a segment of the memory device.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including applying an ordered set of error-handling operations to be performed on data residing in a segment of the memory device as an input to a trained machine learning model, wherein the trained machine learning model is based on latency data for previously-performed error-handling operations; and obtaining an output of the trained machine learning model, the output comprising a reordered set of error-handling operations to be performed on the data residing in the segment of the memory device, and wherein the reordered set adjusts an order of one or more error-handling operations of the ordered set of error-handling operations.

Abstract: Systems and methods are disclosed including a memory and a processing device operatively coupled to the memory. The processing device can perform operations including receiving log data related to a first order of a set of error-handling operations performed on data residing in a segment of a memory device; applying an optimization model to the log data, wherein the optimization model is based on probability data of error recovery and latency data of the set of error-handling operations; and responsive to applying the optimization model to the log data, obtaining, as an output of the optimization model, a second order of the set of error-handling operations, wherein the second order adjusts an order of one or more error-handling operations of the set of error-handling operations in the first order.

Abstract: Systems and methods are disclosed including a memory and a processing device operatively coupled to the memory. The processing device can perform operations including receiving log data related to a first order of a set of error-handling operations performed on data residing in a segment of a memory device; applying an optimization model to the log data, wherein the optimization model is based on probability data of error recovery and latency data of the set of error-handling operations; and responsive to applying the optimization model to the log data, obtaining, as an output of the optimization model, a second order of the set of error-handling operations, wherein the second order adjusts an order of one or more error-handling operations of the set of error-handling operations in the first order.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including applying an ordered set of error-handling operations to be performed on data residing in a segment of the memory device as an input to a trained machine learning model, wherein the trained machine learning model is based on latency data for previously-performed error-handling operations; and obtaining an output of the trained machine learning model, the output comprising a reordered set of error-handling operations to be performed on the data residing in the segment of the memory device, and wherein the reordered set adjusts an order of one or more error-handling operations of the ordered set of error-handling operations.

Abstract: An improved customizable shoe system that enables a wearer to personalize their shoes. The system includes a shoe including a sole and an upper extending about edges of the sole, and one or more transparent enclosures disposed about the shoe. The one or more enclosures each include an opening. A sheet containing one or more inserts corresponding in size and shape to the one or more enclosures, such that a user can remove the one or more inserts to be housed within the one or more enclosures.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: A method of error management includes, in response to a read request for first data from a first storage device of a plurality of storage devices under one or more common data protection schemes, receiving a read uncorrectable indication regarding the first data, obtaining uncorrected data and metadata of an LBA associated with the first data, and obtaining the same LBA from one or more other storage devices of the plurality. The method further includes comparing the uncorrected data with the data and metadata from the other storage devices, speculatively modifying the uncorrected data based, at least in part, on the other data to create a set of reconstructed first data codewords, and, in response to a determination that one of the reconstructed first data codewords has recovered the first data, issuing a write_raw command to rewrite the modified data and associated metadata to the first storage device.