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

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.

Publication Details

Journal
ArXiv.org
Published
2025-02-14
Primary Topic
Low-power high-performance VLSI design
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article

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

ArXiv.org
Low-power high-performance VLSI design
article

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

article en

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.

ArXiv.org
Decent work and economic growth
Openalex Percentile: Top 53%
Low-power high-performance VLSI design
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Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language · ArXiv.org (2025) | TGRS Research Map | TGRS