METHOD AND APPARATUS FOR DETERMINING MEMORY REQUIREMENT IN A NETWORK

    公开(公告)号:US20200257972A1

    公开(公告)日:2020-08-13

    申请号:US16637638

    申请日:2018-08-08

    Abstract: The present disclosure disclose method and apparatus for determining memory requirement for processing a DNN model on a device, a method includes receiving a DNN model for an input, wherein the DNN model includes a plurality of processing layers. The method includes generating a network graph of the DNN model. The method includes creating a colored network graph of the DNN model based on the identified execution order of the plurality of processing layers. The colored network graph indicates assignment of at least one memory buffer for storing at least one output of at least one processing layer. The method includes determining at least one buffer reuse overlap possibility across the plurality of processing layers. Based on the determined at least one buffer reuse overlap possibility, the method includes determining and assigning the memory required for processing the DNN model.

    ON-DEVICE INFERENCE METHOD FOR MULTI-FRAME PROCESSING IN A NEURAL NETWORK

    公开(公告)号:US20240112456A1

    公开(公告)日:2024-04-04

    申请号:US18538723

    申请日:2023-12-13

    CPC classification number: G06V10/82 G06T3/40 G06V10/771

    Abstract: A method for optimizing multi-frame processing model of a neural network includes: receiving a plurality of input frames by a processing engine that is configured to execute a multi frame processing model, the multi frame processing model including a plurality of convolution layers; selecting a pre-determined number of frames from the received plurality of frames for processing by the plurality of convolution layers; determining, as a sequence of frames, at least a preceding frame and a plurality of following frames amongst the selected pre-determined number of frames; removing the preceding frame by processing the sequence of frames using a plurality of filters in the multi frame processing model; and concatenating the plurality of following frames in an order, to the plurality of input frames for subsequent receiving by the multi frame processing model.

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