We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
We consider Discrete Event Systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task mee...
A fundamental problem in timing-driven physical synthesis is the reduction of critical paths in a design. In this work, we propose a powerful new technique that moves (and can als...
Michael D. Moffitt, David A. Papa, Zhuo Li, Charle...
As CMOS devices and operating voltages are scaled down, noise and defective devices will impact the reliability of digital circuits. Probabilistic computing compatible with CMOS o...
Kundan Nepal, R. Iris Bahar, Joseph L. Mundy, Will...
Remote visualization of an arbitrary 2-D planar "cut" from a large volumetric dataset with random access has both gained importance and posed significant challenges over...