PARITY-BASED ERROR MANAGEMENT FOR A PROCESSING SYSTEM

    公开(公告)号:US20240419549A1

    公开(公告)日:2024-12-19

    申请号:US18821203

    申请日:2024-08-30

    Abstract: Methods, systems, and devices for parity-based error management are described. A processing system that performs a computational operation on a set of operands may perform a computational operation, (e.g., the same computational operation) on parity bits for the operands. The processing system may then use the parity bits that result from the computational operation on the parity bits to detect, and discretionarily correct, one or more errors in the output that results from the computational operation on the operands.

    Sequence alignment with memory arrays

    公开(公告)号:US12073110B2

    公开(公告)日:2024-08-27

    申请号:US17931262

    申请日:2022-09-12

    CPC classification number: G06F3/0655 C12Q1/6869 G06F3/0604 G06F3/0673

    Abstract: A memory device may be used to implement a Bloom filter. In some examples, the memory device may include a memory array to perform a multiply-accumulate operation to implement the Bloom filter. The memory device may store multiple portions of a reference genetic sequence in the memory array and compare the portions of the reference genetic sequence to a read sequence in parallel by performing the multiply-accumulate operation. The results of the multiply-accumulate operation between the read sequence and the portions of the reference genetic sequence may be used to determine where the read sequence aligns to the reference sequence.

    ASSOCIATIVE COMPUTING FOR ERROR CORRECTION
    38.
    发明公开

    公开(公告)号:US20230208444A1

    公开(公告)日:2023-06-29

    申请号:US17677593

    申请日:2022-02-22

    CPC classification number: H03M13/1575 G06F11/1068 G11C15/04 H03M13/43

    Abstract: Methods, systems, and devices for associative computing for error correction are described. A device may receive first data representative of a first codeword of a size for error correction. The device may identify a set of content-addressable memory cells that stores data representative of a set of codewords each of which is the size of the first codeword. The device may identify second data representative of the first codeword in the set of content-addressable memory cells. Based on identifying the second data, the device may transmit an indication of a valid codeword that is mapped to the second data.

    Machine learning with feature obfuscation

    公开(公告)号:US11636334B2

    公开(公告)日:2023-04-25

    申请号:US16545837

    申请日:2019-08-20

    Abstract: A system having multiple devices that can host different versions of an artificial neural network (ANN). In the system, inputs for the ANN can be obfuscated for centralized training of a master version of the ANN at a first computing device. A second computing device in the system includes memory that stores a local version of the ANN and user data for inputting into the local version. The second computing device includes a processor that extracts features from the user data and obfuscates the extracted features to generate obfuscated user data. The second device includes a transceiver that transmits the obfuscated user data. The first computing device includes a memory that stores the master version of the ANN, a transceiver that receives obfuscated user data transmitted from the second computing device, and a processor that trains the master version based on the received obfuscated user data using machine learning.

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