Presentation
Accelerated Dynamic Voltage Drop Prediction Using a Lightweight Machine Learning Model
DescriptionAccurate dynamic voltage drop (DVD) analysis is increasingly critical in advanced nodes, where higher densities, lower voltages, and complex packaging exacerbate power delivery challenges. Traditional simulations are computationally expensive and typically performed late in the design cycle, risking costly redesigns. We propose a lightweight ML-based DVD prediction model using multi-scale CNNs, fusion layers, and skip connections to capture spatial and hierarchical power grid features. The model uniquely incorporates package and grid inductance and supports both vectorless and vector-based inputs. Evaluated on a 16 nm RISC-V core, it achieves 80-86 % accuracy with 5 mV tolerance and over 25,000× faster runtime than commercial tools.
Event Type
Work in Progress
TimeMonday, July 275:21pm - 5:21pm PDT
LocationExhibit Hall