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This paper presents an overview of the integration of deep machine learning(DL) in FPGA CAD design flow, focusing on high-level and logic synthesis,placement, and routing. Our analysis identifies key research areas that requiremore attention in FPGA CAD design, including the development of open-sourcebenchmarks optimized for end-to-end machine learning experiences and thepotential of knowledge-sharing among researchers and industry practitioners toincorporate more intelligence in FPGA CAD decision-making steps. By providinginsights into the integration of deep machine learning in FPGA CAD flow, thispaper aims to inform future research directions in this exciting and rapidlyevolving field.

Unraveling the Integration of Deep Machine Learning in FPGA CAD Flow: A Concise Survey and Future Insights