A pure hardware k-SAT solver architecture for FPGA based on generic tree-search

K. Bousmar, F. Monteiro, Z. Habbas, S. Dellagi, A. Dandache
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引用次数: 3

Abstract

SAT (Boolean SATisfiability Problem) is a well studied type of NP-complete problem. Most SAT solvers rely on software implemented tree-search based algorithms. These algorithms, basically sequential or weakly parallel, are most often ineffective when dealing with large scale instances of SAT due to the large search space to be explored in order to find one solution. Despite several improvements and heuristics being proposed for this kind of approach, the fact remains that in real problems, the computational cost continues to be prohibitive. To improve SAT solvers performance, a new trend has emerged in the late years, introducing hardware acceleration. The proposed architectures are in general hybrid, combining software and hardware parts dedicated respectively to the decisional and hard-computional parts of the algorithm. Still, most the hybrid approaches remain constrained by their limited data-access bandwith capacity. In this paper, we propose a new approach, entirely based on hardware and not depending on the SAT-instance to be solved (treated as data). It is fully configurable at synthesis in regard to the level of parallel computation and parallel buffering, as to the size of the SAT instance that can be processed (maximal number of variables and maximal clause length, the total number of clauses being unlimited). One of the main goals is to allow a more effective use of the hardware computational power by reducing the dependence to data contained in low bandwith data storage (such as RAM).
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基于通用树搜索的FPGA纯硬件k-SAT求解器体系结构
SAT(布尔可满足性问题)是一类被广泛研究的np完全问题。大多数SAT求解器依赖于软件实现的基于树搜索的算法。这些算法基本上是顺序的或弱并行的,在处理大规模的SAT实例时通常是无效的,因为为了找到一个解需要探索很大的搜索空间。尽管针对这种方法提出了一些改进和启发式方法,但事实是,在实际问题中,计算成本仍然是令人望而却步的。为了提高SAT求解器的性能,近年来出现了一种新的趋势,即引入硬件加速。所提出的体系结构一般是混合的,将软件和硬件部分分别用于算法的决策部分和硬计算部分。尽管如此,大多数混合方法仍然受到其有限的数据访问带宽容量的限制。在本文中,我们提出了一种新的方法,完全基于硬件,而不依赖于要解决的sat实例(作为数据处理)。在合成时,对于并行计算和并行缓冲的级别,以及可以处理的SAT实例的大小(最大变量数量和最大子句长度,子句总数是无限的),它是完全可配置的。其中一个主要目标是通过减少对包含在低带宽数据存储(如RAM)中的数据的依赖,从而允许更有效地使用硬件计算能力。
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