STORING AND OBTAINING ATTRIBUTE DATA OF ATTRIBUTES OF MACHINE LEARNING MODELS

    公开(公告)号:US20250165816A1

    公开(公告)日:2025-05-22

    申请号:US18747429

    申请日:2024-06-18

    Abstract: In some implementations, a controller may receive a request for an inference. The controller may determine, based on the received request for the inference, a first inference model of a plurality of inference models, to generate the inference. The controller may obtain, from a memory associated with an inference cache, first attribute data regarding first attributes of the first inference model. A location of the first attribute data, in the memory, may be determined using the inference cache. The attributes may include weights associated with the first inference model, biases associated with the first inference model, and a structure of the first inference model. The controller may utilize the first attribute data to generate the inference based on the request.

    GROUPING OF MEMORY CELLS USING A MACHINE LEARNING MODEL RELATED APPLICATION

    公开(公告)号:US20250165397A1

    公开(公告)日:2025-05-22

    申请号:US18731232

    申请日:2024-05-31

    Abstract: A controller may determine, using a machine learning model, reliability characteristic data associated with memory cells of a non-volatile memory device. The machine learning model may be trained using characterization data that identifies different reliability characteristic of one or more non-volatile memory devices. The controller may group, based on the reliability characteristic data, a first portion of the memory cells of the non-volatile memory device in a first management group, and a second portion of the memory cells of the non-volatile memory device in a second management group. The controller may manage, based on the reliability characteristic data, background scanning and logical to physical mapping of the first management group of memory cells, and the second management group of memory cells.

    PREDICTION OF DATA RETENTION DEGRADATION OF A NON-VOLATILE MEMORY DEVICE BASED ON A MACHINE LEARNING ALGORITHM

    公开(公告)号:US20250165148A1

    公开(公告)日:2025-05-22

    申请号:US18622866

    申请日:2024-03-29

    Abstract: A controller, of a solid state drive (SSD), may perform, on one or more blocks of a non-volatile memory device of the SSD, read operations using pre-determined threshold voltages associated with two overlapped charge states. The read operations may be performed after a power-on condition following a power-off condition on the non-volatile memory device. The controller may determine, using a machine learning model, a change in threshold voltages associated with the two overlapped charge states, after the power-off condition. The machine learning model may determine the change in threshold voltages using bit error rates associated with the read operations. The machine learning model may be trained to determine changes in threshold voltages for the two overlapped charge states, after power-off conditions. The controller may determine adjusted threshold voltages associated with the two overlapped charge states based on the change in threshold voltages.

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