首页|Findings from Zhejiang University Provide New Insights into Machine Learning (Dy namic Supply Noise Aware Timing Analysis With Jit Machine Learning Integration)
Findings from Zhejiang University Provide New Insights into Machine Learning (Dy namic Supply Noise Aware Timing Analysis With Jit Machine Learning Integration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Hang zhou, People's Republic of China, by NewsRx correspondents, research stated, "Th e incessant decrease in transistor size has led to reduced voltage noise margins and exacerbated power integrity challenges. This trend intensifies concerns abo ut the efficacy of conventional static timing analysis (STA), which traditionall y assumes a constant power supply level, often resulting in imprecise and overly conservative outcomes." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Zhejiang University, "T o address this, this article proposes a dynamic-noise-aware STA engine enhanced by just-in-time (JIT) machine learning (ML) integration. This approach employs t he Weibull cumulative distribution function (CDF) to accurately represent dynami c power supply noise (PSN). We perform gate-level characterization, assessing de lay and transition time for each timing arc under variations in input transition time, output capacitance, and three PSN-aware parameters. The timing for each t iming arc can then be predicted by a multilayer perceptron (MLP), trained with t he characterization data. Finally, by incorporating JIT compilation techniques, we integrate trained MLP models into the STA engine, achieving both computationa l efficiency and flexibility."
HangzhouPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningZhejiang University
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