Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations a...
Abstract— Not only system performance but also energy efficiency is critically important for embedded systems. Optimal real-time scheduling is effective to not only schedulabili...
This paper presents an efficient modeling scheme and a partitioning heuristic for parallelizing VLSI post-placement timing optimization. Encoding the paths with timing violations...
—On optimizing circuit trajectories, i.e. continuous paths of circuit parameters, the paper presents an auxiliary network approach, which utilizes Pontryagin’s Minimum Principl...