CO-OPERATIVE AND ADAPTIVE MACHINE LEARNING EXECUTION ENGINES

    公开(公告)号:US20230004855A1

    公开(公告)日:2023-01-05

    申请号:US17363856

    申请日:2021-06-30

    Abstract: Techniques for executing machine learning (ML) models including receiving an indication to execute an ML model on a processing core; determining a resource allocation for executing the ML model on the processing core; determining that a layer of the ML model will use a first amount of the resource, wherein the first amount is more than an amount of the resource allocated; determining that an adaptation may be applied to executing the layer of the ML model; executing the layer of the ML model using the adaptation, wherein executing the layer using the adaptation reduces the first amount of the resource used by the layer as compared to executing the layer without using the adaptation; and outputting a result of the ML model based on the executed layer.

    FLEXIBLE HUB FOR HANDLING MULTI-SENSOR DATA

    公开(公告)号:US20210170945A1

    公开(公告)日:2021-06-10

    申请号:US16709548

    申请日:2019-12-10

    Abstract: A hub that receives sensor data streams and then distributes the data streams to the various systems that use the sensor data. A demultiplexer (demux) receives the streams, filters out undesired streams and provides desired streams to the proper multiplexer (mux) or muxes of a series of muxes. Each mux combines received streams and provides an output stream to a respective formatter or output block. The formatter or output block is configured based on the destination of the mux output stream, such as an image signal processor, a processor, memory or external transmission. The output block reformats the received stream to a format appropriate for the recipient and then provides the reformatted stream to that recipient.

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