  Macro placement is the problem of placing memory blocks on a chip canvas. It
can be formulated as a combinatorial optimization problem over sequence pairs,
a representation which describes the relative positions of macros. Solving this
problem is particularly challenging since the objective function is expensive
to evaluate. In this paper, we develop a novel approach to macro placement
using Bayesian optimization (BO) over sequence pairs. BO is a machine learning
technique that uses a probabilistic surrogate model and an acquisition function
that balances exploration and exploitation to efficiently optimize a black-box
objective function. BO is more sample-efficient than reinforcement learning and
therefore can be used with more realistic objectives. Additionally, the ability
to learn from data and adapt the algorithm to the objective function makes BO
an appealing alternative to other black-box optimization methods such as
simulated annealing, which relies on problem-dependent heuristics and
parameter-tuning. We benchmark our algorithm on the fixed-outline macro
placement problem with the half-perimeter wire length objective and demonstrate
competitive performance.
