Parallel branch and bound algorithm for solving integer linear programming models derived from behavioral synthesis

Integer Linear Programming (ILP) formulation of behavioral synthesis allows hardware designers to implement efficient circuits considering resource and timing constraint. However, finding the optimal answer of ILP models is an NP-Hard problem and remains a computational challenge. In this paper, we address this challenge by developing two exact parallel branch and bound algorithms which are capable of solving large-scale ILP models derived from behavioral synthesis. The first algorithm enables sub-node parallelism as well as adaptive branching and memory efficient techniques to accelerate solving ILP models on shared memory multi-core systems. The second algorithm is developed based on node parallelism strategy. We evaluated the proposed algorithms using large ILP models derived from Media Bench Data Flow Graphs. The experimental results indicate both the proposed methods can successfully accelerate behavioral synthesis on multi-core platforms and outperforms IBM ILOG CPLEX (v12.60) MIP solver in solving large ILP models.