SLIDE-x-ML: System-Level Infrastructure for Dataset E-xtraction and Machine Learning Framework for High-Level Synthesis Estimations

Electronic Design Automation (EDA) is a crucial research area related to the development of electronic systems. In particular, High-Level Synthesis (HLS) simplifies HW design by automatically translating C/C++/System C specifications into HW description languages. However, HLS for large systems can be time-consuming. In recent years, Machine Learning (ML) has emerged as a prominent topic in EDA, with numerous studies demonstrating its potential to enhance EDA methods covering nearly all phases of the HW design flow. In such a context, this work presents an approach and related frameworks to collect datasets (i.e., SLIDE-x) useful for performing HLS timing and resource estimation through ML techniques (i.e., SLIDE-x-ML), introducing a data-driven component for feature creation that enhances predictions through various input representations and ML methods.