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.

行为综​​合的整数线性规划（ILP）公式使硬件设计人员可以考虑资源和时序约束来实现高效的电路。但是，找到ILP模型的最佳答案是一个NP-Hard问题，仍然是计算上的挑战。在本文中，我们通过开发两个精确的并行分支定界算法来解决这一挑战，这些算法能够解决从行为综合派生的大规模ILP模型。第一种算法启用子节点并行性以及自适应分支和内存高效技术，以加速求解共享内存多核系统上的ILP模型。第二种算法是基于节点并行性策略开发的。我们使用从媒体基准数据流图得出的大型ILP模型评估了提出的算法。