MEMORY AND TRAINING METHOD FOR NEUTRAL NETWORK BASED ON MEMORY

    公开(公告)号:US20230034366A1

    公开(公告)日:2023-02-02

    申请号:US17388053

    申请日:2021-07-29

    Abstract: The present invention discloses a memory and a training method for neutral network based on memory. The training method includes: obtaining one or more transfer functions of a memory corresponding to one or more influence factors; determining a training plan according to an ideal case and the one or more influence factors; training the neutral network according to the training plan and the one or more transfer functions to obtain a plurality of weights of the trained neutral network; and programming the memory according to the weights.

    MEMORY SYSTEM FOR MAINTAINING DATA CONSISTENCY AND OPERATION METHOD THEREOF

    公开(公告)号:US20230033998A1

    公开(公告)日:2023-02-02

    申请号:US17539257

    申请日:2021-12-01

    Abstract: A memory system for maintaining data consistency and an operation method thereof are provided. The operation method includes: receiving a first data in a first cache of a first memory from a processor; reading the first data from the first cache and writing the first data as a redo log into a log buffer of the first memory; writing the redo log from the log buffer into a memory controller of the processor; performing an in-memory copy in a second memory to copy a second data as an undo log, wherein the second data is an old version of the first data; and writing the redo log from the memory controller into the second memory for covering the second data by the redo log as a third data, wherein the redo log, the third data and the first data are the same.

    MEMORY DEVICE AND COMPUTING METHOD THEREOF

    公开(公告)号:US20230118468A1

    公开(公告)日:2023-04-20

    申请号:US17502067

    申请日:2021-10-15

    Abstract: A memory device and a computing method thereof are provided in the present disclosure. The computing method includes the following steps. A plurality of input-values of a model computation are respectively received through a plurality of first-word-lines of a memory array. Inverted logic values of the input-values are respectively received through a plurality of second-word-lines. The input-values are respectively received through a plurality of first-bit-lines. The inverted logic values are respectively received through a plurality of second-bit-lines. Logic XNOR operation is performed according to each of the input-values and each of the inverted values to obtain a first computation result, and multiplied with one of self-coefficients or one of mutual coefficients of the model computation to obtain a plurality of output-values. The output-values are outputted through a plurality of common-source-lines.

    STORAGE SYSTEM FOR PROCESSING GENOME SEQUENCES

    公开(公告)号:US20240355390A1

    公开(公告)日:2024-10-24

    申请号:US18595672

    申请日:2024-03-05

    CPC classification number: G11C13/02 G16B30/10 G16B50/30

    Abstract: A storage system capable of executing data processing, includes the following elements. A first control unit of a storage device, for cooperating with a sequencer to perform a clustering process on a plurality of original sequences to obtain a plurality of read sequences, generating a plurality of read binary vectors corresponding to the read sequences, and generating a pruned filtering binary vector according to a reference sequence. A first storage module of the storage device, for storing the read binary vectors and the pruned filtering binary vector, and executing an in-memory computing (IMC) according to the read binary vectors and the pruned filtering binary vector, so as to generate a filtered cluster read set. A processing device, for executing an aligning process according to the filtered cluster read set and the reference sequence.

    MEMORY DEVICE AND OPERATION METHOD THEREOF
    5.
    发明申请

    公开(公告)号:US20200349428A1

    公开(公告)日:2020-11-05

    申请号:US16522986

    申请日:2019-07-26

    Abstract: Provided is an operation method for a memory device, the memory device being used for implementing an Artificial Neural Network (ANN). The operation method includes: reading from the memory device a weight matrix of a current layer of a plurality of layers of the ANN to extract a plurality of neuro values; determining whether to perform calibration; when it is determined to perform calibration, recalculating and updating a mean value and a variance value of the neuro values; and performing batch normalization based on the mean value and the variance value of the neuro values.

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