HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond

arXiv:2405.00820v1 Announce Type: cross
Abstract: Machine learning (ML) techniques have been applied to high-level synthesis (HLS) flows for quality-of-result (QoR) prediction and design space exploration (DSE). Nevertheless, the scarcity of accessible high-quality HLS datasets and the complexity of building such datasets present challenges. Existing datasets have limitations in terms of benchmark coverage, design space enumeration, vendor extensibility, or lack of reproducible and extensible software for dataset construction. Many works also lack user-friendly ways to add more designs, limiting wider adoption …

abstract arxiv beyond building challenges complexity cs.ar cs.lg datasets design exploration framework limitations machine machine learning prediction quality space synthesis type