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arXiv · 2503.11662

Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language

Runzhi Wang·Prianka Sengupta·Cristhian Roman-Vicharra·Yiran Chen·Jiang Hu
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Abstract

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 and 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, while significantly reducing turnaround time.

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Runzhi Wang, Prianka Sengupta, Cristhian Roman-Vicharra, Yiran Chen, Jiang Hu. 2025-02-14. Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language. https://arxiv.org/abs/2503.11662

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