Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language
Wang, Runzhi, Sengupta, Prianka, Chen, Yiran, Hu, Jiang
–arXiv.org Artificial Intelligence
In chip design planning, obtaining reliable performance and power forecasts for various design options is of critical importance. Traditionally, this involves using system-level models, which often lack accuracy, or trial synthesis, which is both labor-intensive and time-consuming. We introduce a new methodology, called Lorecast, which accepts English prompts as input to rapidly generate layout-aware performance and power estimates. This approach bypasses the need for HDL code development or synthesis, making it both fast and user-friendly. Experimental results demonstrate that Lorecast achieves accuracy within a few percent of error compared to post-layout analysis.
arXiv.org Artificial Intelligence
Feb-14-2025