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首页> 外文期刊>IEICE Transactions on fundamentals of electronics, communications & computer sciences >Efficient Cut Enumeration Heuristics for Depth-Optimum Technology Mapping for LUT-Based FPGAs
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Efficient Cut Enumeration Heuristics for Depth-Optimum Technology Mapping for LUT-Based FPGAs

机译:基于LUT的FPGA的深度最佳技术映射的有效割枚举启发法

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Recent technology mappers for LUT based FPGAs employ cut enumeration. Although many cuts are often needed to find a good network, enumerating all the cuts with large size consumes a lot of runtime. Existing algorithms employ the bottom-up merging which calculates Cartesian products of the fanins' cuts for each node. The number of cuts is much smaller than the size of the Cartesian products in most cases. Thus, the existing algorithms are inefficient. Furthermore, the number of cuts exponentially increases with the size of cuts, that makes the run-time much longer. Several algorithms to enumerate not all the cuts but partial cuts have been presented [8], [9], but they tend to disturb the quality of networks. This paper presents two algorithms to enumerate cuts; an exhaustive enumeration and a partial enumeration. Both of them are efficient because they do not employ the bottom-up merging. The partial enumeration reduces the number of enumerated cuts with a guarantee that a depth-minimum network can be constructed. The experimental results show that the exhaustive enumeration runs about 5 and 13 times faster than the existing bottom-up algorithm [12] for K = 8,9 respectively, while keeping the same results. On the other hand, the partial enumeration runs about 9 and 29 times faster than the existing algorithm for K = 8, 9, respectively. The average area of networks derived by the sets of cuts enumerated by the partial enumeration is only 4% larger than that derived with using all the cuts, and the depth is the same.
机译:基于LUT的FPGA的最新技术映射器采用割枚举。尽管通常需要很多削减才能找到一个好的网络,但是枚举所有较大规模的削减都需要大量的运行时间。现有算法采用自下而上的合并方法,该合并方法计算每个节点的扇形切口的笛卡尔积。在大多数情况下,切口的数量远小于笛卡尔积的大小。因此,现有算法效率低下。此外,剪切的数量随剪切的大小呈指数增长,这使得运行时间更长。已经提出了一些算法来枚举不是全部切割而是部分切割的算法[8],[9],但是它们往往会干扰网络的质量。本文提出了两种枚举切割的算法。详尽的枚举和部分枚举。两者都是有效的,因为它们不采用自下而上的合并方式。部分枚举减少了枚举的切口数量,并确保可以构建深度最小的网络。实验结果表明,对于K = 8,9,穷举枚举的运行速度分别比现有的自下而上算法[12]快5到13倍,同时保持相同的结果。另一方面,对于K = 8、9,部分枚举的运行速度比现有算法快约9倍和29倍。由部分枚举枚举的割集集合得出的网络平均面积仅比使用所有割集得出的平均面积大4%,并且深度相同。

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