Patents by Inventor Evgeny BLAICHMAN
Evgeny BLAICHMAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11915766Abstract: A method, apparatus, non-transitory computer readable medium, and system for selecting program voltages for a memory device are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may map a set of information bits to voltage levels of one or more memory cells based on a plurality of embedding parameters, program the set of information bits into the one or more memory cells based on the mapping, detect the voltage levels of the one or more memory cells to generate one or more detected voltage levels, and identify a set of predicted information bits based on the one or more detected voltage levels using a neural network comprising a plurality of network parameters, wherein the network parameters are trained together with the embedding parameters.Type: GrantFiled: January 9, 2023Date of Patent: February 27, 2024Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Evgeny Blaichman
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Publication number: 20240045958Abstract: A storage system, including a host device; and a storage device including a nonvolatile memory and at least one processor configured to implement a storage internal protection (SIP) module, wherein the SIP module is configured to: obtain, from the host device, a plurality of storage commands corresponding to the nonvolatile memory, filter the plurality of storage commands to obtain a filtered plurality of storage commands, apply information about the filtered plurality of storage commands to a machine-learning cryptocurrency mining (CM) detection algorithm, and based on the machine-learning CM detection algorithm indicating that a CM operation is detected, provide a notification to the host device.Type: ApplicationFiled: August 2, 2022Publication date: February 8, 2024Applicant: SAMSUNG ELECTRONICS CO., LTD.Inventors: Alexander BUNIN, Evgeny BLAICHMAN, Amit BERMAN
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Publication number: 20230421176Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).Type: ApplicationFiled: July 31, 2023Publication date: December 28, 2023Inventors: Ariel DOUBCHAK, Dikla SHAPIRO, Evgeny BLAICHMAN, Lital COHEN, Amit BERMAN
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Publication number: 20230298674Abstract: An storage device is provided. The storage device includes: a nonvolatile memory; and at least one processor configured to: obtain an input symbol to be stored in a target memory cell among a plurality of memory cells of the nonvolatile memory; obtain cell features of the plurality of memory cells; determine a target voltage for the target memory cell based on the input symbol and the cell features of the plurality of memory cells; and provide the target voltage to the target memory cell.Type: ApplicationFiled: March 17, 2022Publication date: September 21, 2023Applicant: SAMSUNG ELECTRONICS CO., LTDInventors: Amit Berman, Gari Fuks, Evgeny Blaichman
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Patent number: 11742879Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).Type: GrantFiled: October 6, 2021Date of Patent: August 29, 2023Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Ariel Doubchak, Dikla Shapiro, Evgeny Blaichman, Lital Cohen, Amit Berman
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Publication number: 20230207024Abstract: Systems and methods of the present disclosure may be used to improve equalization module architectures for NAND cell read information. For example, embodiments of the present disclosure may provide for de-noising of NAND cell read information using a Multiple Shallow Threshold-Expert Machine Learning Models (MTM) equalizer. An MTM equalizer may include multiple shallow machine learning models, where each machine learning model is trained to specifically solve a classification task (e.g., a binary classification task) corresponding to a weak decision range between two possible read information values for a given NAND cell read operation. Accordingly, during inference, each read sample with a read value within a weak decision range is passed through a corresponding shallow machine learning model (e.g., a corresponding threshold expert) that is associated with (e.g., trained for) the particular weak decision range.Type: ApplicationFiled: March 1, 2023Publication date: June 29, 2023Inventors: Amit Berman, Evgeny Blaichman, Ron Golan, Sergey Gendel
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Publication number: 20230147137Abstract: A method, apparatus, non-transitory computer readable medium, and system for selecting program voltages for a memory device are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may map a set of information bits to voltage levels of one or more memory cells based on a plurality of embedding parameters, program the set of information bits into the one or more memory cells based on the mapping, detect the voltage levels of the one or more memory cells to generate one or more detected voltage levels, and identify a set of predicted information bits based on the one or more detected voltage levels using a neural network comprising a plurality of network parameters, wherein the network parameters are trained together with the embedding parameters.Type: ApplicationFiled: January 9, 2023Publication date: May 11, 2023Inventors: Amit Berman, Evgeny Blaichman
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Patent number: 11626168Abstract: Systems and methods of the present disclosure may be used to improve equalization module architectures for NAND cell read information. For example, embodiments of the present disclosure may provide for de-noising of NAND cell read information using a Multiple Shallow Threshold-Expert Machine Learning Models (MTM) equalizer. An MTM equalizer may include multiple shallow machine learning models, where each machine learning model is trained to specifically solve a classification task (e.g., a binary classification task) corresponding to a weak decision range between two possible read information values for a given NAND cell read operation. Accordingly, during inference, each read sample with a read value within a weak decision range is passed through a corresponding shallow machine learning model (e.g., a corresponding threshold expert) that is associated with (e.g., trained for) the particular weak decision range.Type: GrantFiled: March 10, 2021Date of Patent: April 11, 2023Assignee: SAMSUNG ELECTRONICS CO.. LTD.Inventors: Amit Berman, Evgeny Blaichman, Ron Golan, Sergey Gendel
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Patent number: 11587620Abstract: A method, apparatus, non-transitory computer readable medium, and system for selecting program voltages for a memory device are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may map a set of information bits to voltage levels of one or more memory cells based on a plurality of embedding parameters, program the set of information bits into the one or more memory cells based on the mapping, detect the voltage levels of the one or more memory cells to generate one or more detected voltage levels, and identify a set of predicted information bits based on the one or more detected voltage levels using a neural network comprising a plurality of network parameters, wherein the network parameters are trained together with the embedding parameters.Type: GrantFiled: June 5, 2020Date of Patent: February 21, 2023Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Evgeny Blaichman
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Patent number: 11546000Abstract: A mobile electronic device may include a memory device and a memory controller including an error correction code (ECC) encoder to encode data, a constrained channel encoder configured to encode an output of the ECC encoder based on one or more constraints, a reinforcement learning pulse programming (RLPP) component configured to identify a programming algorithm for programming the data to the memory device, an expectation maximization (EM) signal processing component configured to receive a noisy multi-wordline voltage vector from the memory device and classify each bit of the vector with a log likelihood ration (LLR) value, a constrained channel decoder configured to receive a constrained vector from the EM signal processing component and produce an unconstrained vector, and an ECC decoder configured to decode the unconstrained vector. A machine learning interference cancellation component may operate based on or independent of input from the EM signal processing component.Type: GrantFiled: May 4, 2020Date of Patent: January 3, 2023Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Ariel Doubchak, Eli Haim, Evgeny Blaichman
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Patent number: 11527299Abstract: A method, apparatus, non-transitory computer readable medium, and system for using an error correction code in a memory device with a neural network are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may receive a signal from a physical channel, wherein the signal is based on a modulated symbol representing information bits encoded using an error correction coding scheme, extract features from the signal using a feature extractor trained using probability data collected from the physical channel, and decode the information bits with a neural network decoder taking the extracted features as input.Type: GrantFiled: June 3, 2020Date of Patent: December 13, 2022Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Evgeny Blaichman, Ron Golan
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Patent number: 11481624Abstract: A NAND memory device that includes a plurality of blocks, each block comprises a plurality of wordlines and an associated agent, and each wordline comprises a plurality of cells and a plurality of voltage levels and an associated agent, and each voltage level comprises an agent. A method of programming the NAND memory device includes receiving, by an agent at a given rank in the plurality of ranks, parameters from a higher rank agent in the hierarchy of ranks and a state from the memory device; determining, by the agent, an action from the parameters and the state; passing the action as parameters to a lower rank agent in the hierarchy of ranks; and updating the agent based on a reward output by the agent, wherein the reward measures a difference between the target voltage levels of the cells and the actual voltage levels programmed to the cells.Type: GrantFiled: October 4, 2019Date of Patent: October 25, 2022Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Evgeny Blaichman, Amit Berman, Elisha Halperin, Dan Elbaz
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Publication number: 20220293192Abstract: Systems and methods of the present disclosure may be used to improve equalization module architectures for NAND cell read information. For example, embodiments of the present disclosure may provide for de-noising of NAND cell read information using a Multiple Shallow Threshold-Expert Machine Learning Models (MTM) equalizer. An MTM equalizer may include multiple shallow machine learning models, where each machine learning model is trained to specifically solve a classification task (e.g., a binary classification task) corresponding to a weak decision range between two possible read information values for a given NAND cell read operation. Accordingly, during inference, each read sample with a read value within a weak decision range is passed through a corresponding shallow machine learning model (e.g., a corresponding threshold expert) that is associated with (e.g., trained for) the particular weak decision range.Type: ApplicationFiled: March 10, 2021Publication date: September 15, 2022Inventors: AMIT BERMAN, Evgeny Blaichman, Ron Golan, Sergey Gendel
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Publication number: 20220116057Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).Type: ApplicationFiled: October 6, 2021Publication date: April 14, 2022Inventors: Ariel DOUBCHAK, Dikla SHAPIRO, Evgeny BLAICHMAN, Lital COHEN, Amit BERMAN
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Patent number: 11232842Abstract: A method for determining an optimal threshold of a nonvolatile memory device, the method including: reading a page from a nonvolatile memory device with a default threshold and attempting to hard decode the page using the default threshold; reading the page two more times with a predetermined offset voltage when the hard decoding fails and attempting to soft decode the page using the default threshold; approximating an empirical distribution of successfully decoded bits with a Gaussian distribution for each level; finding an intersection of the Gaussian distributions; and setting the intersection as a new reading threshold and reading the page again with the new reading threshold.Type: GrantFiled: November 18, 2020Date of Patent: January 25, 2022Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Elisha Halperin, Evgeny Blaichman, Amit Berman
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Publication number: 20220013189Abstract: A memory system including a memory device and a memory controller including a processor. The memory controller is configured to read outputs from the memory cells in response to a read command from a host and to convert the read outputs to a first codeword. The processor performs a first error correcting code (ECC) operation on the first codeword. The processor is further configured to apply, for each selected memory cell among the memory cells, a corresponding one of the read outputs and at least one related feature as input features to a machine learning algorithm to generate a second codeword, and the memory controller is configured to perform a second ECC operation on the second codeword, when the first ECC operation fails.Type: ApplicationFiled: July 8, 2020Publication date: January 13, 2022Inventors: AMIT BERMAN, EVGENY BLAICHMAN, RON GOLAN, SERGEY GENDEL
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Patent number: 11221769Abstract: A memory system includes a memory device, and a memory controller including a processor and an internal memory. A computer program including a neural network is stored in the memory system. The processor executes the computer program to extract a voltage level from each of a plurality of memory cells connected to one string select line (SSL), in which the memory cells and the SSL are included in a memory block of the memory device, provide the voltage levels as input to the neural network, and perform noise cancellation on the SSL, using the neural network, by changing at least one of the voltage levels from a first voltage level to a second voltage level. The first voltage level is classified into a first cluster of memory cells, and the second voltage level is classified into a second cluster of memory cells different from the first cluster.Type: GrantFiled: September 27, 2019Date of Patent: January 11, 2022Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Elisha Halperin, Evgeny Blaichman
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Patent number: 11205498Abstract: A memory system including a memory device and a memory controller including a processor. The memory controller is configured to read outputs from the memory cells in response to a read command from a host and to convert the read outputs to a first codeword. The processor performs a first error correcting code (ECC) operation on the first codeword. The processor is further configured to apply, for each selected memory cell among the memory cells, a corresponding one of the read outputs and at least one related feature as input features to a machine learning algorithm to generate a second codeword, and the memory controller is configured to perform a second ECC operation on the second codeword, when the first ECC operation fails.Type: GrantFiled: July 8, 2020Date of Patent: December 21, 2021Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Amit Berman, Evgeny Blaichman, Ron Golan, Sergey Gendel
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Publication number: 20210383887Abstract: A method, apparatus, non-transitory computer readable medium, and system for using an error correction code in a memory device with a neural network are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may receive a signal from a physical channel, wherein the signal is based on a modulated symbol representing information bits encoded using an error correction coding scheme, extract features from the signal using a feature extractor trained using probability data collected from the physical channel, and decode the information bits with a neural network decoder taking the extracted features as input.Type: ApplicationFiled: June 3, 2020Publication date: December 9, 2021Inventors: Amit Berman, Evgeny Blaichman, Ron Golan
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Publication number: 20210383871Abstract: A method, apparatus, non-transitory computer readable medium, and system for selecting program voltages for a memory device are described. Embodiments of the method, apparatus, non-transitory computer readable medium, and system may map a set of information bits to voltage levels of one or more memory cells based on a plurality of embedding parameters, program the set of information bits into the one or more memory cells based on the mapping, detect the voltage levels of the one or more memory cells to generate one or more detected voltage levels, and identify a set of predicted information bits based on the one or more detected voltage levels using a neural network comprising a plurality of network parameters, wherein the network parameters are trained together with the embedding parameters.Type: ApplicationFiled: June 5, 2020Publication date: December 9, 2021Inventors: AMIT BERMAN, Evgeny Blaichman