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Datapath merging is an efficient approach to reduce hardware resources and configuration time in the synthesis of digital systems. In order to solve datapath merging, we have to find the maximum weighted clique, which is an NP-hard problem. So, datapath merging is a time-consuming process. In this article, we use OpenMP library to perform divide and conquer task parallelism to find the maximum weighted clique. Therefore, considerable reduction in the synthesis time and almost linear speedup has been achieved. The experimental results obtained from running this algorithm on different benchmarks represent speedup ranging from 1.2 times to 6.5 times for an 8-core system. [ABSTRACT FROM AUTHOR]

Modeling high-level synthesis (HLS) as mixed integer linear programming (MILP) affords the opportunity to integrate constraints and optimization objectives of hardware design in the form of a mathematical intermediate representation. Consequently, it is possible to improve previously developed methods to solve MILP models and customize them for application in domain-specific functions. However, the problem remains NP-hard, and solving large models requires methods that investigate the state space intelligently. Despite the high potential of branch and bound (B&B) algorithms to solve MILP models quickly, computing the best answer is still an open challenge. In this paper, we first develop three model improvement techniques that reduce the size of the search space for MILP models derived from HLS. Then, we present a new B&B algorithm to tackle the computational challenge by considering the properties of the original HLS problem. In this regard, we propose two heuristic techniques that enable the B&B algorithm to prove that some branches are not promising and should be pruned. Moreover, we have developed a best-first strategy by suggesting a new priority key calculation scheme to improve tree traversal in the B&B algorithm. Besides, the sifting method has been employed as the LP relaxation method to achieve fast processing of large models. Our proposed approach was evaluated using a set of MILP models derived from the synthesis of Mediabench data flow graphs. The experimental results indicate that our approach outperforms modern MILP solvers in terms of speed and the scale of the MILP models which can solve. According to the test results on the large MILP models, we solved models with 7254 integer and binary variables in less than 13 minutes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]