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 this 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 storage system is configured to use a binary full-depth symmetrically-sorted tree to infer a read threshold based on a plurality of parameters of the memory.

Abstract: A data storage device and method for dynamic prediction of random read with low memory consumption are provided. In one embodiment, a data storage device comprises a volatile memory, a non-volatile memory, and a controller. The controller is configured to allocate an amount of space in the volatile memory for a history pattern matcher data structure used to predict next read commands from a host to read data stored in the non-volatile memory; determine an accuracy of the predicted next read commands; and based on the determined accuracy, dynamically allocate a different amount of space in the volatile memory for the history pattern matcher data structure. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

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 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 this 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.

Abstract: A method may include: applying a first voltage on at least one first terminal of a first direct current (DC) bus electrically connected to a power source, obtaining at least one indication that discharge of a second voltage related to the first voltage should be performed, and discharging the second voltage by electrically connecting at least one second terminal of a second DC bus to a ground in response to the at least one indication. Another method may include: injecting a current at at least one terminal of a direct current (DC) bus that is electrically connected to a power source, simultaneous to injecting the current, measuring an insulation relative to ground, obtaining an electrical parameter related to the power source, and, in response to the electrical parameter, maintaining the current injected at the terminal of the DC bus without ceasing the measuring of the insulation relative to a ground.

Abstract: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to program key value (KV) pair data to the memory device, where the KV pair data includes a key and a value, analyze the key, and generate metadata based on the analyzing. The controller is further configured to generate a metadata index for a plurality of KV pair data, where the metadata index value corresponds to a similarity or a difference between a first key and a second key, and cluster generated metadata based on the metadata index. The controller is further configured to receive a read command for the KV pair data, analyze the generated metadata of the KV pair data, generate a predicted next key, and use read look ahead (RLA) to read a predicted next KV pair data based on the predicted next key.

Abstract: The present disclosure generally relates 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: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to receive key value (KV) pair data, determine an entropy value of the received KV pair data, select an error correction code (ECC) code rate based on the determined entropy value, and program the KV pair data to a codeword (CW). The KV pair data includes a key and a value. The programming includes encoding the KV pair data using the selected ECC code rate. The controller is further configured to aggregate a portion of another KV pair data and the KV pair data and program the aggregated KV pair data to the CW using a selected ECC code rate.

Abstract: A storage system supports several memory mappings that translate data bits into different physical voltage levels in its non-volatile memory. The storage system receives a selection of one of the memory mappings from a host, which makes the selection based on an application or expected workload of the host. The storage system uses the selected memory mapping for a memory access operation, such as a read operation or a write operation.

Abstract: A data storage device includes a memory device and a controller coupled to the memory device. The controller is further configured to receive a key value (KV) pair data, determine a size of a value length and a size of a target wordline of the memory device for programming of the KV pair data, determine a size of residual data, store the residual data in a location separate from the target wordline and the KV pair data minus the residual data to the target wordline, and read the residual data from the location separate and the target wordline data in response to a read command for the KV pair data. The size of the value length is greater than the size of the target wordline. The size of the residual data is the size of the value length minus the size of the target wordline.

Abstract: The present disclosure generally relates to estimating when data to be written will be read or re-written prior to actually writing the data to the memory device. The estimating can be used to smartly route the data to the appropriate memory location at the writing stage or to evict the data from a hot memory location to a colder memory location. To perform the estimating, typical traces or data may be used as may the metadata of the data. Separating data according to the data “temperature” (i.e. the expected access time and frequency), and usage to optimize the SLC partition usage has meaningful impact on several storage metrics such as performance and endurance.

Abstract: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to receive key value (KV) pair data, group a plurality of KV pair data based on a data clustering value, aggregate the grouped plurality of KV pair data, and program the aggregated plurality of KV pair data to the memory device. A length of the KV pair data is less than a size of a flash management unit (FMU). The KV pair data includes a key and a value. Each KV pair data of the plurality of KV pair data has a length less than the size of the FMU. The received KV pair data is stored in a temporary location and grouped together in the temporary location. The grouping is based on a similarity of characteristics of plurality of KV pair data.

Abstract: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to determine an error correction code (ECC) code length for KV pair data and/or an ECC code rate for the KV pair data, where the ECC code length and the ECC code rate are selected according to a value length and decoding capability of the KV pair data, generate ECC parity based on the selecting, and program the KV pair data and the generated ECC parity to the memory device.

Abstract: Interleaved ECC coding for key-value data storage devices. In one embodiment, a controller includes a memory interface including a namespace database; an ECC engine; a controller memory; and an electronic processor. The electronic processor is configured to receive a host write command, determine whether write access was setup as a key-value (KV) namespace in the namespace database and is associated with the host write command, and control the ECC engine and the memory interface to perform one or more program operations on the data in the memory using the interleaved ECC coding and based on the host write command in response to determining that the write access was setup as the KV namespace in the namespace database and the KV namespace is associated with the host write command.

Abstract: A method may include: applying a first voltage on at least one first terminal of a first direct current (DC) bus electrically connected to a power source, obtaining at least one indication that discharge of a second voltage related to the first voltage should be performed, and discharging the second voltage by electrically connecting at least one second terminal of a second DC bus to a ground in response to the at least one indication. Another method may include: injecting a current at at least one terminal of a direct current (DC) bus that is electrically connected to a power source, simultaneous to injecting the current, measuring an insulation relative to ground, obtaining an electrical parameter related to the power source, and, in response to the electrical parameter, maintaining the current injected at the terminal of the DC bus without ceasing the measuring of the insulation relative to a ground.

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: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to create a dual parity matrix. The dual parity matrix includes a full parity form that includes a payload, a first parity portion, and a second parity portion and a reduced parity form that includes the payload and the first parity portion. The second parity portion is 0. The controller is further configured to create an incremental parity construction matrix. The incremental parity construction matrix includes two arrays. A first array includes a first payload portion, a first, first parity portion, and a first, second parity portion and a second array includes a second payload portion, a second, first parity portion, and a second, second parity portion. The incremental parity construction matrix is arranged in either a block triangular construction or a block diagonal construction.

Abstract: A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to store a plurality of codewords in the memory device. Each codeword of the plurality of codewords includes host data and parity data corresponding to the host data. Less than all of the plurality of codewords further includes statistics corresponding to the host data. Each statistic of the plurality of codewords is the same or different as another statistic of the plurality of codewords. The statistics are either incremental statistics, adaptive statistics, or both incremental statistics and adaptive statistics.

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 to optimize these different usage types upon detecting these proof of space blockchain activities. These optimizations can include suspending one or more background or other garbage collection activities. Additional optimizations can further include configuring partitions or namespaces to comprise single-level-cell majority or single-level-cell only memory devices to increase writing speeds. Further optimizations can include interleaving or extending the length of error correction codes.

Abstract: The present disclosure generally relates to data storage devices, such as solid state drives (SSDs), and efficient data storage device operations related to power loss incidents. A controller of the data storage device is configured to periodically pre-encode data that is stored in random access memory (RAM), detect a power loss event, and program the data and parity data to non-volatile memory (NVM) in response to detecting the power loss event. Upon reaching a threshold size, the data in RAM may be pre-encoded and the pre-encoded data can be programmed to the RAM or the NVM. The parity data may be stored in one or more locations of the NVM. Upon detecting a power loss event, any data remaining in RAM that is not pre-encoded is encoded. The data and any parity data not yet programmed to the NVM are programmed to the NVM.