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.

    METHOD OF PERFORMING CODE REVIEW AND RELATED SYSTEM

    公开(公告)号:US20250147863A1

    公开(公告)日:2025-05-08

    申请号:US18935662

    申请日:2024-11-04

    Applicant: MEDIATEK INC.

    Abstract: A method of performing code review and a code review system are provided. The code review system includes a code repository, a static scanning tool, an analytical neural network and a generative neural network. The code repository is configured to store an original source code and a new code created by a developer in response to a code change request to merge the new code with the original source code. The static scanning tool is configured to collect data associated with each commit in the new code. The analytical neural network is implemented with an analytical AI and configured to assess a risk level of each commit in the new code. The generative neural network is implemented with a generative AI and configured to provide a code summarization and an initial code review comment of each commit in the new code.

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