3D POINT CLOUD-BASED DEEP LEARNING NEURAL NETWORK ACCELERATION APPARATUS AND METHOD

    公开(公告)号:US20230376756A1

    公开(公告)日:2023-11-23

    申请号:US18199995

    申请日:2023-05-22

    CPC classification number: G06N3/08

    Abstract: Disclosed is a 3D point cloud-based deep learning neural network acceleration apparatus including a depth image input unit configured to receive a depth image, a depth data storage unit configured to store depth data derived from the depth image, a sampling unit configured to sample the depth image in units of a sampling window having a predetermined first size, a grouping unit configured to generate a grouping window having a predetermined second size and to group inner 3D point data by grouping window, and a convolution computation unit configured to separate point feature data and group feature data, among channel-direction data of 3D point data constituting the depth image, to perform convolution computation with respect to the point feature data and the group feature data, to sum the results of convolution computation by group grouped by the grouping unit, and to derive the final result.

    MULTI-AGENT REINFORCEMENT LEARNING SYSTEM AND OPERATING METHOD THEREOF

    公开(公告)号:US20230334329A1

    公开(公告)日:2023-10-19

    申请号:US18157983

    申请日:2023-01-23

    CPC classification number: G06N3/092 G06N3/0495

    Abstract: The present disclosure provides a system for accelerating multi-agent reinforcement learning through sparsity processing and an operating method thereof and proposes an acceleration system, which can analyze a weight pruning algorithm capable of guaranteeing accuracy suitably for characteristics of multi-agent reinforcement learning and includes an on-chip encoding unit, a sparse weight workload allocation unit, and sparsity parallel processing architecture through vector processing, which can effectively support the weight pruning algorithm, and an operating method of the system. Furthermore, the present disclosure proposes an acceleration platform that constitutes a circuit in a way to be suitable for a deep learning model from its initial step while having high throughput and power efficiency by using an FPGA, not a GPU in which several thousands of cores have been integrated and which generate many and consume great power.

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