Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration

    公开(公告)号:US20220138570A1

    公开(公告)日:2022-05-05

    申请号:US17495489

    申请日:2021-10-06

    Applicant: MediaTek Inc.

    Abstract: A system performs the operations of a neural network agent and a circuit simulator for analog circuit sizing. The system receives an input indicating a specification of an analog circuit and design parameters. The system iteratively searches a design space until a circuit size is found to satisfy the specification and the design parameters. In each iteration, the neural network agent calculates measurement estimates for random sample generated in a trust region, which is a portion of the design space. Based on the measurement estimate, the system identifies a candidate size that optimizes a value metric. The circuit simulator receives the candidate size and generates a simulation measurement. The system calculates updates to weights of the neural network agent and the trust region for a next iteration based on, at least in part, the simulation measurement.

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