ML-based power analysis for ASIC IP development
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurately estimating power consumption is critical for designing Application-Specific
Integrated Circuits (ASICs), especially as they grow more complex. This thesis investigates
the application of machine learning (ML) for ASIC power analysis. We
establish a comprehensive flow encompassing dataset generation and power simulation.
Subsequently, we develop and evaluate several ML models, categorized as
architecture-based and flow-based approaches. Our findings reveal varying degrees
of success among these models, with some demonstrating strong predictive capabilities
while others exhibit limitations. Finally, we propose potential avenues for
future research to address the identified challenges and further enhance the accuracy
of ML-based power prediction.
Beskrivning
Ämne/nyckelord
Machine Learning, ASIC Power consumption