DEEP NEURAL NETWORK WORKLOAD SCHEDULING
    1.
    发明申请

    公开(公告)号:US20190266015A1

    公开(公告)日:2019-08-29

    申请号:US15906963

    申请日:2018-02-27

    Abstract: Systems, methods, and computer-executable instructions for scheduling neural network workloads on an edge device. A performance model for each neural network model is received. Parameters for each neural network workload is determined based on an associated performance model. Processing core assignments are determined from a plurality of processing cores for each neural network workload based on the corresponding performance model and processing core utilization. Image streams are received and associated with a neural network workload. Each neural network workload is scheduled to run on the processing cores based on the processing core assignments.

    Deep neural network workload scheduling

    公开(公告)号:US10942767B2

    公开(公告)日:2021-03-09

    申请号:US15906963

    申请日:2018-02-27

    Abstract: Systems, methods, and computer-executable instructions for scheduling neural network workloads on an edge device. A performance model for each neural network model is received. Parameters for each neural network workload is determined based on an associated performance model. Processing core assignments are determined from a plurality of processing cores for each neural network workload based on the corresponding performance model and processing core utilization. Image streams are received and associated with a neural network workload. Each neural network workload is scheduled to run on the processing cores based on the processing core assignments.

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