MACHINE LEARNING OPITIMIZATION SYSTEM FOR CODEBOOK REFINEMENT IN INTRACHIP COMMUNICATIONS

    公开(公告)号:US20250045251A1

    公开(公告)日:2025-02-06

    申请号:US18919459

    申请日:2024-10-18

    Abstract: A system and method for optimizing intrachip communication using machine learning-based codebook refinement is presented. The system employs an on-chip machine learning model to continuously analyze data patterns and update a codebook used for data compression in intrachip communication. Key aspects may comprise real-time data collection, feature extraction, performance monitoring, and gradual codebook updates. The system adapts to evolving data patterns, improving compression efficiency over time. A fallback mechanism ensures system stability by reverting to a conservative codebook if performance degrades. Security measures, including cryptographic signatures for updates and anomaly detection, are integrated. The system optimizes power consumption by adjusting operations based on the chip's power state. This adaptive approach significantly enhances intrachip communication efficiency, potentially improving overall chip performance and energy efficiency. The system's design allows for efficient execution within the constraints of on-chip resources, making it suitable for implementation in various multi-core processor architectures.

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