Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Dudy David Avraham

Ran Zamir

Alexander Bazarsky

Eran Sharon

Omer Fainzilber

Ariel Navon

Idan Alrod

Stella Achtenberg

Shay Benisty

Yehuda Hahn

Alex Bazarsky

Evgeny Mekhanik

Idan Goldenberg

Nika Yanuka

Karin Inbar

David Rozman

Rohit Sehgal

Mark Shlick

Tomer Tzvi Eliash

Gadi Vishne

Ofir Pele

Yoav Markus

Yan Dumchin

Eyal Sobol

Yuri Ryabinin

Arthur Shulkin

Eran Banani

Vered Kelner

Tsiko Shohat Rozenfeld

Igal Mariasin

Rotem Feinblat

Tommer Kuper Lotan

Martin Booth

Gilad Koren

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.

Data storage device and method for inferring a read threshold using a time tag determination

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.

Data storage device and method for predicting future read thresholds

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.

Generating and using a state transition matrix for decoding data in a DNA-based storage system

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.

Storage system and method for circuit-bounded-array-based time and temperature tag management and inference of read thresholds

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.

Error correction systems and methods for DNA storage

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.

Cold storage partition management in proof of space blockchain systems

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.

DNA storage error correction code architecture for optimized decoding

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.

Calibrating state transition probabilities associated with a DNA-based storage system to optimize decoding

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.

Storage system and method for implementation of symmetric tree models for read threshold calibration

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.

Storage system and method for inference of read thresholds based on memory parameters and conditions

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.

Partial speed changes to improve in-order transfer

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.

Storage device throttling in proof of space blockchain systems

The present disclosure generally relates to improving adaptive tuning of different clock rates of a memory device. Rather than clock rates only being determined off of one parameter such as workload, the clock rates now will be determined using multiple parameters. The tuning may be based on system parameters to allow the system to withstand challenges that arise during the operation. The clock frequency table is maintained in the device controller. The table holds the clock frequency of each component. The disclosure proposes modifying the table according to different system environment parameters to maintain performance or reduce power consumption. Adaptive tuning allows a more flexible system design that can adapt according to the current system status. Adaptive tuning also reduces peak power consumption, improves performance, and better quality of service (QoS) compatibility characteristics.

Adaptive tuning of memory device clock rates based on dynamic parameters

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.

Hot-cold chunks soft-prediction during programming

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.

Growing community of inventors

Even Yehuda, Israel

Dudy David Avraham