DYNAMIC COMPRESSION AND SPECIALIZATION OF A MACHINE LEARNING MODEL

    公开(公告)号:US20250036933A1

    公开(公告)日:2025-01-30

    申请号:US18225371

    申请日:2023-07-24

    Abstract: In one embodiment, a device identifies a plurality of tasks that a base machine learning model is able to perform. The device receives, via a user interface, a request to generate a specialized model to perform a particular task for deployment to a target deployment environment. The device uses knowledge distillation on the base machine learning model to train the specialized model to perform the particular task based on at least one of the plurality of tasks. The device causes the specialized model to be deployed to the target deployment environment.

    EFFICIENT SCALING OF PARTITIONED NEURAL NETWORK INFERENCE

    公开(公告)号:US20250094823A1

    公开(公告)日:2025-03-20

    申请号:US18368801

    申请日:2023-09-15

    Abstract: In one implementation, a controller determines performance of a partitioned neural network. The controller identifies, based on the performance, a particular partition of the partitioned neural network as a bottleneck. The controller configures a first device to execute a replica of the particular partition. The controller configures a multiplexer that provides an output of the particular partition or the replica of the particular partition as input to a downstream partition of the partitioned neural network.

    TRUSTED EXECUTION ENVIRONMENT FOR DISTRIBUTED DATA SECURITY IN THE SERVICE MESH

    公开(公告)号:US20250132918A1

    公开(公告)日:2025-04-24

    申请号:US18381835

    申请日:2023-10-19

    Abstract: In one implementation, a method is disclosed comprising: associating, by a device in a service mesh, a security function with a portion of an online application that is executed in a distributed manner across the service mesh; executing, by the device, the security function and the portion of the online application within a trusted execution environment of the device to produce output data; generating, by the device, a cryptographic proof for the output data based on the security function; and providing, by the device, the output data and the cryptographic proof to a remote execution environment within the service mesh to establish a verifiable data lineage for the output data.

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