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Fast and Near-Optimal Timing-Driven Cell Sizing under Cell Area and Leakage Power Constraints Using a Simplified Discrete Network Flow Algorithm

机译:使用简化的离散网络流算法在小区面积和泄漏功率约束下快速且接近最佳时序驱动的小区大小

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We propose a timing-driven discrete cell-sizing algorithm that can address total cell size and/or leakage power constraints. We model cell sizing as a “discretized” mincost network flow problem, wherein available sizes of each cell are modeled as nodes. Flow passing through a node indicates the choice of the corresponding cell size, and the total flow cost reflects the timing objective function value corresponding to these choices. Compared to other discrete optimization methods for cell sizing, our method can obtain near-optimal solutions in a time-efficient manner. We tested our algorithm on ISCAS’85 benchmarks, and compared our results to those produced by an optimal dynamic programming- (DP-) based method. The results show that compared to the optimal method, the improvements to an initial sizing solution obtained by our method is only 1% (3%) worse when using a 180 nm (90 nm) library, while being 40–60 times faster. We also obtained results for ISPD’12 cell-sizing benchmarks, under leakage power constraint, and compared them to those of a state-of-the-art approximate DP method (optimal DP runs out of memory for the smallest of these circuits). Our results show that we are only 0.9% worse than the approximate DP method, while being more than twice as fast.
机译:我们提出了一种时序驱动的离散单元大小调整算法,该算法可以解决总单元大小和/或泄漏功率约束。我们将单元的大小建模为“离散的”最小成本网络流问题,其中将每个单元的可用大小建模为节点。通过节点的流指示相应单元大小的选择,总流成本反映与这些选择相对应的时序目标函数值。与其他离散优化方法相比,我们的方法可以以省时的方式获得接近最优的解决方案。我们在ISCAS'85基准测试中测试了我们的算法,并将我们的结果与基于最佳动态规划(DP-)的​​方法得出的结果进行了比较。结果表明,与最佳方法相比,使用我们的方法获得的初始上浆解决方案的改进仅比使用180 nm(90 nm)库时差了1%(3%),而提高了40-60倍。我们还获得了在泄漏功率约束下ISPD’12电池尺寸基准测试的结果,并将其与最新的近似DP方法的结果进行了比较(对于这些电路中的最小电路,最佳DP用尽了内存)。我们的结果表明,我们只比近似DP方法差0.9%,而速度却快两倍以上。

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