Abstract: The present disclosure describes apparatuses and methods for contextual search of a storage system. In some aspects, a metadata manager of a storage system receives a query to search the data stored on the storage media of the apparatus. The metadata manager identifies an entry in a relational database of the metadata manager that includes a label that is relevant to the query and determines, based on the entry in the relational database, a reference address of a target node in a navigational database of the metadata manager that corresponds to the label. As results for the query to search, the metadata manager returns an object of the target node at the reference address in the navigational database and corresponding objects of relative nodes connected to the target node via respective links. By so doing, the metadata database may enable contextual or implicit search of data in the storage system.

Abstract: The present disclosure describes apparatuses and methods for implementing an adaptive low-density parity check (LDPC) decoder. In various aspects, an adaptive LDPC decoder processes a first portion of data using first parameters effective to change a status of the LDPC decoder. The LDPC decoder selects second parameters of the LDPC decoder based on the status of the LDPC decoder. The LDPC decoder then processes a second portion of the data with the LDPC decoder using the second parameters and provides decoded data of the channel based on at least the processing the first portion of the data using the first parameters and the processing of the second portion of the data using the second parameters. By adaptively altering the decoding parameters based the status of the decoder, the adaptive LDPC decoder may decode channel data in fewer decoding iterations or with a higher success rate, thereby improving LDPC decoding performance.

Abstract: This disclosure describes a storage aggregator controller with metadata computation control. The storage aggregator controller communicates, via a host interface, over a computer network with one or more remote hosts, and also communicates, via a storage device interface, with a plurality of local storage devices, which are separate from the remote host(s) and which have respective non-volatile memories. The storage aggregator controller manages the local storage devices for storage or retrieval of media objects. The storage aggregator controller also governs a selective computation, at aggregator control circuitry or at a storage device controller of one or more of the storage devices, of metadata that defines content characteristics of the media objects that are retrieved from the plurality of storage devices or that are received from the one or more hosts over the computer network for storage in the plurality of storage devices.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.

Abstract: Systems and methods for selecting an optimal error recovery procedure for correcting a read error in a solid-state drive are provided. A machine learning model is trained to forecast which error recovery procedure of a plurality of error recovery procedures is most likely to achieve a predetermined goal given a state of a solid-state drive. The predetermined goal is based on at least one of a read latency and a failure rate of the solid-state drive. A current state of the solid-state drive is determined. An error recovery procedure is selected from among the plurality of error recovery procedures by inputting the current state of the solid-state drive into the trained machine learning model, thereby triggering the trained machine learning model to output the selected error recovery procedure. The selected error recovery procedure is executed to recover data from the solid-state drive.

Abstract: The present disclosure describes apparatuses and methods for machine learning-enabled (ML-enabled) management of storage media access. In some aspects, an ML-enabled storage controller obtains features of available blocks of storage media of a storage media system. The controller can receive, from a host system, a request to write data and determine features of the data to be written to the storage media. The controller provides the respective features of the available blocks and the data to a neural network and receives, from the neural network, a selected block of the available blocks for writing of the data. The selected block may include an ML-optimized selection from the available blocks based on the features of both the available blocks and the data. The controller then writes the data of the request to the ML-selected block of storage media of the storage media system, which may improve storage media performance.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Abstract: A method for decoding a codeword includes partitioning the codeword into a plurality of component codewords where each of the component codewords comprising a respective plurality of bits. The respective plurality of bits in each of the plurality of component codewords are interleaved. Each of the plurality of interleaved component codewords are decoded along two dimensions to produce (i) a set of first decoding results and (ii) a set of second decoding results. A short error event is then detected and corrected based on (i) the set of first decoding results and (ii) the set of second decoding results.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Abstract: Systems and methods for selecting an optimal error recovery procedure for correcting a read error in a solid-state drive are provided. A machine learning model is trained to forecast which error recovery procedure of a plurality of error recovery procedures is most likely to achieve a predetermined goal given a state of a solid-state drive. The predetermined goal is based on at least one of a read latency and a failure rate of the solid-state drive. A current state of the solid-state drive is determined. An error recovery procedure is selected from among the plurality of error recovery procedures by inputting the current state of the solid-state drive into the trained machine learning model, thereby triggering the trained machine learning model to output the selected error recovery procedure. The selected error recovery procedure is executed to recover data from the solid-state drive.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: A storage control device coupled to a storage device and located remotely from a host device receives media object data from the host device. The storage control device identifies a type of the media object data and select, based on the identified type, a computational model from among a plurality of computational models for use by a computational engine of the storage control device. The computational engine uses the selected computational model to generate metadata describing the media object data. The metadata is stored in the storage device so as to be selectively retrievable from the storage device separately from the media object data.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.

Abstract: A method for storing data in groups of logical data sectors across a plurality of contiguous data tracks of a data storage medium includes defining a plurality of interleaver patterns, each interleaver pattern including a sector interleaving pattern specifying a respective order in which segments of respective ones of the logical data sectors are spread across physical data sectors of a respective one of the contiguous data tracks. For each respective group of logical data sectors, a respective interleaver pattern is selected. Data is written from each group of logical data sectors to one of the data tracks using the selected interleaver pattern. Each respective group of logical data sectors is written to its respective data track using a different interleaver pattern from any other group of logical data sectors written to an adjacent data track, so that no two adjacent data tracks are written using the same interleaver pattern.

Abstract: A method for performing error recovery for data stored on a track of a storage device, in which the method includes: receiving a request to read the data from the storage device, identifying a plurality of sectors of the track to be read in response to the request, reading the data from the plurality of sectors of the track and parity data, based on the data read from the plurality of sectors, determining whether any of the plurality of sectors corresponds to a failed sector, and recovering a portion of the data from the failed sector using the parity data and portions of the data stored in remaining ones of the plurality of sectors.