In the era of diminishing returns from technology scaling, domain-specific accelerators have become a key component in today’s computing platforms. The natural evolution of this trend will lead to an increasing volume and diversity of accelerators for emerging applications such as machine learning, robotics, data centers, and many more. However, today’s accelerators are largely designed in isolation, with little consideration of how they interact with software and other components, leading to suboptimal end-to-end performance.
In this talk, I will discuss the challenges and opportunities associated with the design of next-generation domain-specific systems. Specifically, I will describe our group's recent work on domain-specific accelerator design, integration, and simulation, with the goal of democratizing accelerated computing for all applications. The results of our endeavors indicate a future in which domain-specific systems can be thoroughly designed, integrated, and evaluated to achieve high efficiency across different applications.
Professor Sophia Shao is an Assistant Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Previously, she was a Senior Research Scientist at NVIDIA and received her Ph.D. degree in 2016 from Harvard University. Her research interests are in the area of computer architecture, with a special focus on domain-specific architecture, deep-learning accelerators, and high-productivity hardware design methodology. Her work has been awarded a Distinguished Artifact Award at ISCA’2023, the Best Paper Award at DAC’2021, the Best Paper Award at JSSC’2020, a Best Paper Award at MICRO’2019, a Research Highlight of Communications of ACM (2021), Top Picks in Computer Architecture (2014), and Honorable Mentions (2019*2). She is a recipient of an NSF CAREER Award, the 2022 IEEE TCCA Young Computer Architect Award, an Intel Rising Star Faculty Award, a Google Research Scholar Award, and the inaugural Dr. Sudhakar Yalamanchili Award.
Her personal webpage is https://people.eecs.berkeley.edu/~ysshao/.