Structured models often achieve excellent performance but can be slow at test time. We investigate structure compilation, where we replace structure with features, which are often...
Abstract. The paper describes an evolutionary algorithm for the general nonlinear programming problem using a surrogate model. Surrogate models are used in optimization when model ...
—A unified optimization framework is presented for simultaneous gate sizing and placement. These processes are unified using Lagrangian multipliers, which synchronize the efforts...
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
In this paper, we propose an adaptive hybrid dynamic power management (AH-DPM) strategy based on a predictive shutdown scheme and an adaptive non-stationary stochastic process. Th...