Macro placement is the problem of placing memory blocks on a chip canvas.
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive cost functions on permutations.
In experiments, we demonstrate the improved sample efficiency of GP BO using FM kernels (BO-FM). On synthetic problems and hyperparameter optimization problems, BO-FM outperforms competitors consistently.
On this combinatorial graph, we propose an ARD diffusion kernel with which the GP is able to model high-order interactions between variables leading to better performance.
A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space.