Architecture 2.0: Challenges and Opportunities

Machine learning driven computer architecture tools and methods have the potential to drastically shape the future of computer architecture. The question is: how can we lay the foundation to effectively usher in this era? In this post, we delve into the transformative impact of machine learning (ML) on the research landscape, emphasizing the importance of understanding both its potential and pitfalls to fully support ML-assisted computer architecture research. By exploring these advancements, our aim is to highlight the opportunities that lie ahead and outline the collective steps that we, as a community, can take towards realizing the era of "Architecture 2.0."