Abstract: Multiply and accumulate (MAC) operations typically involve extremely large amounts of data and large numbers of operations. As such, they are extremely computationally intensive, involving large numbers of data transfers and consuming large amounts of time and power. To address these problems, the following presents methods of realizing a MAC engine in a 3D NAND flash die. The engine takes as input two vectors and outputs their dot product. The dot product of two vectors is the building block of matrix multiplication. The 3D NAND MAC engine presented here can be used to implement modern machine learning algorithms, in particular Neural Networks. The two vector operands are not programed into the NAND memory cells, therefore the endurance of the device is not compromised.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A client device encodes one or more input datasets of real numbers into a plaintext polynomial with integral coefficients that do not include an imaginary component and generates an input ciphertext by encrypting the plaintext polynomial according to a Fully Homomorphic Encryption (FHE) scheme. The input ciphertext includes at least encrypted coefficients of an input polynomial. A server receives the input ciphertext and performs a convolution on the input ciphertext using a kernel by at least in part separately multiplying the input polynomial by one or more kernel polynomials to result in one or more corresponding convolved polynomials. The one or more kernel polynomials include kernel coefficients encoded using kernel values for the kernel. At least a plurality of coefficients is used from each of the one or more convolved polynomials to derive an output ciphertext representing an output of the convolution on the input ciphertext using the kernel.

Abstract: A client device encodes at least two datasets using one or more encoding functions to generate encoded data portions that are encrypted using a first key according to an approximate Fully Homomorphic Encryption (FHE) scheme to generate encrypted data portions that are sent to a plurality of servers. Encrypted results are received from at least a subset of servers of the plurality of servers. Each encrypted result is calculated by a respective server using at least two encrypted data portions received by the server. The encrypted results are decrypted using a secret key according to the approximate FHE scheme to derive decrypted encoded results that are decoded using an approximate decoding function. In one aspect, an encrypted result is calculated by each server by evaluating a multivariate function using the at least two encrypted data portions received by the server.

Abstract: 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.

Abstract: 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.

Abstract: System, methods, and devices described herein utilize blockchain systems to tie physical storage devices to a digital non-fungible token (NFT). By utilizing this association, the manufacturer can provide software to assure purchasers that their devices are genuine. Furthermore, ownership transfers can be verified and utilized as a means to provide accurate provenance of a storage device, which can increase the value of the device in certain collectible situations. Associating a storage device to an NFT can include pair the drive with a unique identifier provided by the manufacturer, and an address that consists of an encryption key and a secret key that only the owner will know. A user can then encrypt their data and transfer ownership of the storage device to a new owner who will generate a new encryption key. The new owner cannot access data stored on the device, preventing the original owner’s data from being compromised.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, and devices described herein achieve a more efficient plotting method for proof of space cryptocurrency processes, such as the Chia cryptocurrency system. Storage devices that are configured for use with proof of space cryptocurrency processing can be configured to utilize less space within a memory array during the initial farming or plotting process. This can be done by engaging in one or more data redundancy processes during the forward propagation step of table generation. Furthermore, additional efficiency can be achieved by using a modified backward propagation method during plotting that looks back fewer steps than traditional methods. Finally, additional efficiency can be achieved by utilizing modified compression methods as well as changing the configurations of the park settings within the tables. By utilizing these modified techniques and tuning them for increased efficiency, the amount of space and resources needed to generate new cryptocurrency plots can be reduced.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (?) to the Shannon entropy estimate, to determine a value for the argument parameter (?) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (?).