A Parallel Branch-and-Cut Approach for Detailed Placement

We introduce a technique that utilizes distributing computing resources for the efficient optimization of a traditional physical design problem. Specifically, we present a detailed placement strategy designed to exploit distributed computing environments, where the additional computing resources are employed in parallel to improve the optimization time. A Mixed Integer Programming (MIP) model and branch-and-cut optimization strategy are employed to solve the standard cell placement problem. By exploiting the problem structure, our algorithm improves upon the solutions afforded by existing optimization algorithms. First, an efficient batch-branching technique can eliminate several integer decision variables during each step of the optimization procedure. This batch-branching scheme can be performed serially or in parallel. In addition, custom cutting-planes are shown to significantly reduce the run time for optimizations as they efficiently refine the feasible region in order to quickly produce integer solutions. Our serial branch-and-cut strategies allow for significant reductions in wirelength, relative to the state-of-the-art commercial software package CPLEX, assuming a fixed allotment of time. Furthermore, we show that distributed computing resources can be used to significantly reduce the time required to achieve reductions in wirelength.