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    MULTI-INPUT MULTI-OUTPUT ADDER AND OPERATING METHOD THEREOF
    4.
    发明公开
    MULTI-INPUT MULTI-OUTPUT ADDER AND OPERATING METHOD THEREOF 审中-公开

    公开(公告)号：US20230144030A1

    公开(公告)日：2023-05-11

    申请号：US17546074

    申请日：2021-12-09

    Applicant: Industrial Technology Research Institute

    Inventor： Chih-Wei Liu ,  Yu-Chuan Li

    IPC: G06F7/50 ,  G06F7/523 ,  G06F7/483

    CPC classification number: G06F7/50 ,  G06F7/523 ,  G06F7/483

    Abstract: A multi-input multi-output adder and an operating method thereof are proposed. The multi-input multi-output adder includes an adder circuitry configured to perform an operation. The operation includes the following. A first source operand and a second source operand are added to generate a first summed operand. Direct truncation is performed on at least one last bit of the first summed operand to generate a first truncated-summed operand. Right shift is performed on the first truncated-summed operand to generate a first shifted-summed operand. A bit number of the right shift of the first truncated-summed operand is equal to a bit number of the direct truncation of the first summed operand.

    ALGORITHM SYSTEM OF DEEP REINFORCEMENT LEARNING AND ALGORITHM METHOD THEREOF
    5.
    发明申请
    ALGORITHM SYSTEM OF DEEP REINFORCEMENT LEARNING AND ALGORITHM METHOD THEREOF 有权

    公开(公告)号：US20250165793A1

    公开(公告)日：2025-05-22

    申请号：US18393464

    申请日：2023-12-21

    Applicant: Industrial Technology Research Institute

    Inventor： Chia-Hsiang Yang ,  Shih-Hao Chen ,  Chih-Wei Liu

    IPC: G06N3/092

    Abstract: An algorithm method for deep reinforcement learning includes initializing an environment and a model; executing an experience collection process and a network update process in parallel, and determining whether the experience collection process and the network update process have reached a termination condition; and continuing executing the experience collection process and the network update process in parallel in response to neither of the experience collection process and the network update processes has met the termination conditions; and stopping executing the experience collection process and the network update process in response to one of the experience collection processes and the network update process having met the termination conditions. The experience collection process includes obtaining a current state of the environment; calculating to determine the current action based on the current observation values according to a current policy of the model; and returning the current action to the environment.

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