Western Digital Technologies, Inc.

Hgst Netherlands, B.v.

Hitachi Global Storage Technologies Netherlands B.v.

Sandisk Technologies Inc.

Cyril Guyot

Zvonimir Z Bandic

Robert Eugeniu Mateescu

Qingbo Wang

Luiz M Franca-Neto

Lluis Pamies-Juarez

Dejan Vucinic

Yuval Cassuto

Adam Manzanares

Yongjune Kim

Marco Sanvido

Timothy Tsai

Jorge Campello De Souza

Won Ho Choi

Anand Kulkarni

Frank R Chu

Hessam Mahdavifar

Tomohiro Harayama

Filip Blagojevic

Shashank Agrawal

Erhard Theodor Schreck

Bruce Alexander Wilson

Bruno Jean Marchon

Yoocharn Jeon

Richard M H New

Ivana Djurdjevic

Shad Henry Thorstenson

Ken Qing Yang

Dongyang Li

Qing Li

Arman Fazeli Chaghooshi

Dongwoo Kim

Jonathan Darrel Coker

Takaki Nakamura

Hitoshi Kamei

Lidu Huang

Qing Dai

Ashwin Narasimha

Viacheslav Anatolyevich Dubeyko

Xing-Cai Guo

Adam Michael Espeseth

Koen De Keyser

Mark Christiaens

Mohammed Ghiath Khatib

Martin Chen

Evan Drake

Noah Watkins

Certain aspects of the present disclosure provide techniques for performing machine learning, including: processing a training data instance with a task model to generate an encoding and a task model output; processing a discriminator input based on the encoding using a discriminator model to generate an estimated mutual information between the encoding and the one or more input variables of the training data instance; updating parameters of the discriminator model using a first iterative optimization algorithm to maximize a discriminator objective function based on the estimated mutual information; and updating parameters of the task model using a second iterative optimization algorithm to minimize a task objective function based on a sum of the estimated mutual information between the task model output and the one or more input variables of the training data instance and a conditional entropy between the target variable and an encoding generated by the task model.

Adversarial information bottleneck strategy for improved machine learning

A storage device minimizes redundancy for stuck bit codes when writing a message to a memory device. A controller generates a set of masks that are codewords and that include values that correspond to stuck bit values. The set of masks may include full length codewords or shortened codewords. When the controller receives a message including a predefined number of label bits and determines that a location in the memory device where the message is be stored includes two stuck bits, the controller encodes the message to produce a first message codeword. The controller then locates a mask from the set of masks, wherein when the mask is added to the first message codeword, any two stuck message bits are masked. The controller computes a final message codeword using the first message codeword and the mask and stores the final message codeword on the memory device.

Minimizing redundancy for stuck bit coding

Systems, methods, and devices described herein can configure a cryptocurrency wallet or other blockchain-based account to be self-aware and alert the owner of the wallet to one or more potentially fraudulent situations occurring with at least one account they own. The wallet may be a hardware-based cryptocurrency wallet or may be a blockchain-based account operating by an external financial institution that allows for the management of cryptocurrency assets. The self-aware wallet can be configured to track all relevant previously known and/or approved transactions associated with a user's private cryptocurrency key. A subsequent scan on one or more blockchains is performed to detect new transactions associated with the user's private key. If a newly detected blockchain transaction is not in the list of previously known or approved user transactions, a potential compromise may be occurring. Once detected, the wallet can generate a notification to the user alerting them to the issue.

Self-aware cryptocurrency wallets

A storage device processes storage-free stuck bits information when writing and reading stored on the memory device. A controller encodes the data with cyclic error-correcting codes to generate a codeword and determines that a location in the memory device where codeword is be stored includes a stuck bit. Rather than storing the stuck bits information, when storing the codeword, the controller generates an encoding mask, adds the encoding mask to the codeword to generate encoded data, and stores the encoded data on the memory device. When reading the encoded data, the controller generates a list of decoding masks including the encoding mask, goes through the lists and adds a decoding mask to the encoded data. The controller decodes the encoded data with the encoding mask from the list and returns the data.

Stuck bits encoding and decoding on a storage device

Blockchain systems operate over a network of computing devices. Proof of space blockchain consensus systems utilize data stored in storage devices across the computing devices within the network. These storage devices are utilized to generate and store proof of space consensus data. This data is then accessed at a later time to respond to challenges issued across the blockchain network. In order to limit successful submissions of these challenge responses, one or more filters are utilized. These filters result in only a fraction of the stored data on a storage device to be useable for solving the blockchain challenge. Attackers may attempt to circumvent this filter to increase their odds of submitting an approved solution to the blockchain challenge. In order to address this, additional data structures are stored within the storage device and are registered at the time of creation on the blockchain to make these filters more robust.

Consensus data filtering in proof of space blockchain systems

Certain aspects of the present disclosure provide techniques for proving possession of data in a storage device participating in a distributed data storage network. An example storage device includes a storage circuitry and a trusted circuit. The storage circuitry is configured to store a plurality of data blocks. The trusted circuit generally has a private signing key securely stored thereon. The trusted circuit is generally configured to compute a hash over data stored in a plurality of data blocks and to generate an anonymous digital signature for the data stored in the plurality of data blocks based at least in part on the private signing key and the computed hash. The trusted circuit may be interposed on a write path to the storage circuitry such that data written to the storage circuitry is processed through the trusted circuit.

Trusted systems for decentralized data storage

Techniques for storage-free message authentication for error-correcting-codes are disclosed. A storage controller of a storage device receives a request to encode a message in a format having an error-correcting code schema that generates a parity code. A key generator generates a pseudorandom transposition of the message and the parity code as a first part of a secret key. A pseudorandom character string is determined as a second part of the secret key. The output of the pseudorandom transposition and the pseudorandom character string are combined to generate the encoded message which is returned in response to the request. The secret key associated with the message is stored in non-volatile memory.

Storage-free message authenticators for error-correcting-codes

Blockchain systems operate over a network of computing devices. Proof-of-space blockchain consensus systems utilize data (called plots) stored in storage devices across the computing devices within the network. These storage devices are utilized to generate and store proof-of-space consensus data. This data is then accessed at a later time to respond to challenges issued across the blockchain network. The owner of a plot may wish to sell a plot to another miner. If the seller is a bad-faith actor, they may retain copies of the secret key(s) and use them to continue mining the plot along with the buyer. To prevent these attacks, it may be desirable to submit a challenge response block where the proof-of-space is not visible as part of the challenge response. This may be done by replacing the proof-of-space with a proof-of-knowledge.

Hiding proof-of-space in blockchain systems

Various processes for efficiently and effectively managing huge pages include a process for optimizing memory deduplication of huge pages, optimizing the promotion of one or more base pages to one or more huge pages and optimizing memory compaction of a memory space associated with a huge page.

Optimizing huge page management

Certain aspects of the present disclosure provide a method for performing multimode inferencing, comprising: receiving machine learning model input data from a requestor; processing the machine learning model input data with a machine learning model using processing hardware at a first power level to generate first output data; selecting a second power level for the processing hardware based on comparing the first output data to a threshold value; processing the machine learning model input data with the machine learning model using the processing hardware at the second power level to generate second output data; and sending second output data to the requestor.

Variable power mode inferencing

Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (), generated by the random number generator (), into blocks () of a length (L), estimate a Shannon entropy of a second sub-set () of the blocks (), using a first sub-set () of the blocks () to initialize the estimating, solve an estimate function, that relates an argument parameter (&#x3b8;) to the Shannon entropy estimate, to determine a value for the argument parameter (&#x3b8;) that is indicative of a probability of a most probable block being generated by the random number generator () as a new block, and use the length (L) to tune an estimate of randomness of the random number generator () calculated based on the value for the argument parameter (&#x3b8;).

Efficient estimator of min-entropy

A system and method for allocating computational resources includes a plurality of classifiers, a memory array, and a memory controller to allocate memory from the memory array to each of the plurality of classifier. The system and method also include an optimization processor to determine an optimized bit precision value for at least one of the plurality of classifiers based upon a relative importance of the plurality of classifiers. The memory controller allocates the memory from the memory array to the plurality of classifiers based upon the determined optimized bit precision value.

Computational resource allocation in ensemble machine learning systems

An apparatus is provided that includes a memory device including a plurality of sub-arrays, and a memory controller. The memory controller is configured to determine a value of a parameter of a corresponding write pulse for each bit of a word based on a relative importance of each bit, and write each bit of the word to a corresponding one of the plurality of sub-arrays using the corresponding write pulses.

Apparatus and methods for writing random access memories

Modifying a page stored in a non-volatile storage includes receiving one or more requests to modify data stored in the page with new data. One or more lines are identified in the page that include data to be modified by the one or more requests. The identified one or more lines correspond to one or more respective byte ranges each of a predetermined size in the page. Encoded data is created based on the new data and respective locations of the one or more identified lines in the page. The encoded data is cached, and at least a portion of the cached encoded data is used to rewrite the page in the non-volatile storage to include at least a portion of the new data.

Page modification encoding and caching

A system and method include determining, by a memory controller associated with a memory device, a value of a parameter of a write pulse for a plurality of bits of a B-bit word to be stored in the memory device. The value of the parameter is based upon a relative importance of a bit position of the plurality of bits in the B-bit word to a performance of a machine learning or signal processing task involving the B-bit word, a fidelity metric, and a resource metric. The system and method also include writing each of the plurality of bits of the B-bit word in a different sub-array of the memory device using the write pulse generated based on the value of the parameter determined for a particular one of the plurality of bits.

Write efficiency in magneto-resistive random access memories

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San Jose, CA, United States of America

Cyril Guyot