Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
With increasing design complexity, as well as continued scaling of supplies, the design and analysis of power/ground distribution networks poses a difficult problem in modern IC d...
We present STAR, a self-tuning algorithm that adaptively sets numeric precision constraints to accurately and efficiently answer continuous aggregate queries over distributed data...
Navendu Jain, Michael Dahlin, Yin Zhang, Dmitry Ki...
—Current implementations of real-time collaborative applications rely on a dedicated infrastructure to carry out all synchronizing and communication functions, and require all en...
Krzysztof Rzadca, Jackson Tan Teck Yong, Anwitaman...
— As the scale and complexity of parallel systems continue to grow, failures become more and more an inevitable fact for solving large-scale applications. In this research, we pr...