As the circuit size increases in modern electronics, the design process becomes more complicated. Even though the hardware design process is divided into multiple phases, many of the divided problems are still extremely time consuming to solve. One of these NP-hard problems is the routing problem. As electronics step into the deep submicron era, optimizing the routing becomes increasingly important.
One of the methods to solve global routing is to formulate the problem as an integer programming (IP) problem. This formulation can then be relaxed into a linear programming problem and solved using interior point method. This thesis investigates two new approaches to optimize the speed of solving global routing using Karmarkar’s interior point method, as well as the effect of combining various optimizations with these new approaches. The first proposed approach is to utilize solution stability as the interior point loop converges, and attempt to remove solutions that have already stabilized. This approach reduces the problem size and allows subsequent interior point iterations to proceed faster. The second proposed approach is to solve the inner linear system (projection step) in interior point method in parallel.
Experimental results show that for large routing problems, the performance of the solver is improved by the optimization approaches. The problem reduction stage allows for great speedup in the interior point iterations, without affecting the quality of the solution significantly. Furthermore, the timing required to solve inner linear system in the interior point method is improved by solving the problem in parallel. With these optimizations, solving the routing problem using the IP formation becomes increasingly more efficient. By solving an efficient parallel IP formation rather than a traditional sequential approach, more efficient optimal solutions which incorporate multiple conflicting objectives can be achieved
