Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Sandisk Il Ltd.

Ariel Navon

Shay Benisty

Alexander Bazarsky

Yehuda Hahn

Eran Sharon

Idan Alrod

Alon Marcu

Alex Bazarsky

Omer Fainzilber

Ran Zamir

Daniel Joseph Linnen

Tz-Yi Liu

Tianhong Yan

Karin Inbar

Gadi Vishne

Ofir Pele

David Avraham

Stella Achtenberg

Opher Lieber

Noam Presman

Rami Rom

William Bernard Boyle

Idan Goldenberg

Ofer Shapira

Joshua Lehmann

David Gur

Nian Niles Yang

Itzhak Pomerantz

Ishai Ilani

Tomer Tzvi Eliash

Dudy David Avraham

Philip David Reusswig

Tomer Eliash

Rahav Yairi

Alon Eyal

Nika Yanuka

Roi Kirshenbaum

Menahem Lasser

Christopher John Petti

Simon Litsyn

Yoav Weinberg

Xinde Hu

Julian Vlaiko

Gopinath Balakrishnan

David Rozman

Yan Dumchin

Yuri Ryabinin

Yair Baram

Danny Berler

Wanfang Tsai

Itshak Afriat

Arthur Shulkin

Aki Bleyer

Alexander Kalmanovich

Micha Yonin

Yoseph Hassan

Guruswamy Ganesh

Chris Petti

Alexander Tsang-Nam Chu

Tsiko Shohat Rozenfeld

Edgar Barber

Yonatan Karlik

Roy Peretz

Didi Gur

Vered Kelner

Data storage devices configured to exploit generative-adversarial-networks (GANs). The GANs include super-resolution GANs (SRGANs). In some examples, a GAN-based 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.

Data storage device configured for use with a generative-adversarial-network (GAN)

A data storage device and method for device-initiated hibernation are provided. In one embodiment, the data storage device comprises a non-volatile memory and a controller. The controller is configured to: receive, from a host during a set-up phase of a hibernation process, a plurality of write commands with a current state of a volatile memory in the host; store the plurality of write commands in a queue, wherein the plurality of write commands are not executed during the set-up phase of the hibernation process; receive a trigger from the host to perform an execution phase of the hibernation process; and in response to receiving the trigger, execute the plurality of write commands to store the current state of the host's volatile memory in the non-volatile memory of the data storage device. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

Data storage device and method for device-initiated hibernation

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 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.

Instead of incorporating a single interface towards the host for transferring data, utilizing a designated write-only storage logging device. The write-only storage logging device can accept sequential streams and automatically overwrite. The controller will read the log material in a secure manner using a different and separate physical connection than the one used for write. The storage device may have LBA ranges that work as write-only as well as other LBA ranges, which are normal (both reads and writes are enabled). Both options will allow for a traditional file system as well as sharing the storage, but will still protect the log areas that would be used for events that should not be read out.

Read-protected storage device with sequential logging

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.

Adaptive use of multiple channels in a storage device

Recurrent Neural Networks (RNNs) wherein a non-volatile memory (NVM) array provides a memory bank for the RNN. The RNN may include a Neural Turning Machine (NTM) and the memory bank may be an NTM matrix stored in the NVM array. In some examples, a data storage device (DSD) that controls the NVM array includes both a data storage controller and a separate NTM controller. The separate NTM controller accesses the NTM matrix of the NVM array directly while bypassing flash translation layer (FTL) components of the data storage controller. Additionally, various majority wins error detection and correction procedures are described, as well as various disparity count-based procedures.

Memory (NVM) devices for use with recurrent neural networks

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.

Optimized power up in storage memory devices

The present disclosure generally relates to improved handling of write commands. The host memory buffer (HMB) or other storage space can be utilized to delay execution of host write commands which will improve write performance in different use cases and will also allow having more concurrent streams than open blocks without impacting write or read performance. Generally, once a write command is received, the write command is revised as a new write command that is logically equivalent to the original write command. The revised write command is moved to the HMB along with the data. In so doing, the write command is coalesced and write command handling is improved.

Write coalescing via HMB to optimize write performance

A data storage device and method for race-based data access in a multiple host memory buffer system are provided. In one embodiment, the data storage device stores data in a plurality of host memory buffers in the host instead of in just the host memory buffer usually associated with the data. To read the data, the data storage device sends read commands to all of the host memory buffers. That way, even if some of the host memory buffers are busy, the data can be returned from another one of the host memory buffers. In future reads in similar workloads, a read command can be sent to the host memory buffer that returned the data. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

Data storage device and method for race-based data access in a multiple host memory buffer system

Instead of a system with no awareness to the specific properties of the described system files, such as atomicity of different types of system files, utilize the special characteristics of the corresponding system files to optimize storage handling. A host marks a certain logical block address (LBA) range as belonging to an atomic file. That entire range will be treated as a single atomic unit. Conversely, an LBA range being used to append to a log file may have very small atomic units, allowing for incremental updates without changing the atomicity of the rest of the media. When a write command is passed, the write command will have a certain length. Depending on the length of the write command, the device can disassemble the write command into smaller write sectors of the smallest possible write portion. The device will then write the small write portions to a storage location, while keeping an atomic principle of each of the small write portions.

SSD optimization awareness to atomicity of different system files

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

Methods and apparatus for power management in data storage devices are provided. One such data storage device (DSD) includes a non-volatile memory (NVM), a set of hardware processing engines, and a power sensor to detect a total power consumption of the set of hardware processing engines. A processor is configured to determine a power-per-processing event value for each of the set of processing engines based on total power consumption measurements, then control delivery of power to the processing engines based on the power-per-processing event values in accordance with a power budget. In some examples, the DSD employs a least-squares procedure to estimate the power-per-processing event values so the values can be determined without needing to measure the individual power consumption of the processing engines. Exemplary processing engines include a Read engine, a Write engine, etc. A recursive least-squares update procedure is also described.

Power-per-processing event estimates based on total power consumption measurements within a data storage device

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

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.

Reordering commands to optimize execution of the commands

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.

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Revava, Israel

Ariel Navon