Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
We develop a projected-subgradient primal-dual Lagrange optimization for global placement, that can be instantiated with a variety of interconnect models. It decomposes the origin...
There has been extensive research focused on maximizing the throughput of wireless networks in general and mesh networks in particular. Recently, techniques have been developed th...
The main contribution of this work is an analytical model for finding the upper bound on the temperature difference among various locations on the die. The proposed model can be u...
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...