Legalization algorithm for multiple-row height standard cell design

Chow, Wing-Kai; Pui, Chak-Wa; Young, Evangeline F. Y.

doi:10.1145/2897937.2898038

Cited by 44 publications

(20 citation statements)

References 8 publications

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“…For example, Wang et al [7] adapt Abacus to handle such cells, whereas Hung et al [14] use a network flow model similar to [10] and [13] while legalizing each bin using an Integer Linear Programming (ILP) model. Meanwhile, Chow et al [16] propose a new algorithm that enumerates valid insertion points for cells and chooses the one that results in minimum displacement. Chen et al [3] introduce a new model for the problem as a Linear Complementary Problem by relaxing some of the problem constraints.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Multi-Threaded Legalization Through $k$-d Tree Circuit Partitioning

Fabre

Güntzel

Pilla

et al. 2018

2018 31st Symposium on Integrated Circuits and Systems Design (SBCCI)

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In the physical synthesis of integrated circuits the legalization step may move all circuit cells to fix overlaps and misalignments. While doing so, it should cause the smallest perturbation possible to the solution found by previous optimization steps to preserve placement quality. Legalization techniques must handle circuits with millions of cells within acceptable runtimes, besides facing other issues such as mixed-cell-height and fence regions. In this work we propose a k-d tree data structure to partition the circuit, thus removing data dependency. Then, legalization is sped up through both input size reduction and parallel execution. As a use case we employed a modified version of the classic legalization algorithm Abacus. Our solution achieved a maximum speedup of 35 times over a sequential version of Abacus for the circuits of the ICCAD2015 CAD contest. It also provided up to 10% reduction on the average cell displacement.

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Section: Related Workmentioning

confidence: 99%

Enhancing Multi-Threaded Legalization Through $k$-d Tree Circuit Partitioning

Fabre

Güntzel

Pilla

et al. 2018

2018 31st Symposium on Integrated Circuits and Systems Design (SBCCI)

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“…Several recent works have been proposed to legalize circuits from advanced technology nodes, which contain multi-row cells. For example, the works from [4] and [18] propose algorithms that legalize cells one at a time. Their algorithms enumerate a set of valid insertion points for each cell and place each in the location that minimizes circuit displacement.…”

Section: Related Workmentioning

confidence: 99%

“…Their algorithms enumerate a set of valid insertion points for each cell and place each in the location that minimizes circuit displacement. While the algorithm from [4] uses a greedy heuristic, the algorithm from [18] adapts the Abacus legalization algorithm [16] that uses dynamic programming.…”

Section: Related Workmentioning

confidence: 99%

“…This way, they can still achieve better results than the previous works, but with a more reasonable run time. Finally, the work of [15] improves over [4] and considers additional metrics for legalization problem, such as routability constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Since smaller partitions are the majority in the partitioning strategy, it is better to have a higher accuracy for them. Table 4 shows the results of applying the pruning strategy presented in Section 4 on the validation and test circuits with relation to average displacement, maximum displacement and half-perimeter wirelength (HPWL) 4 . Observe that none of the circuits presented in this table were used for the model training.…”

Section: Integration With Circuit Partitioningmentioning

confidence: 99%

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How Deep Learning Can Drive Physical Synthesis Towards More Predictable Legalization

Netto

Fabre

Fontana

et al. 2019

Proceedings of the 2019 International Symposium on Physical Design

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Machine learning has been used to improve the predictability of different physical design problems, such as timing, clock tree synthesis and routing, but not for legalization. Predicting the outcome of legalization can be helpful to guide incremental placement and circuit partitioning, speeding up those algorithms. In this work we extract histograms of features and snapshots of the circuit from several regions in a way that the model can be trained independently from region size. Then, we evaluate how traditional and convolutional deep learning models use this set of features to predict the quality of a legalization algorithm without having to executing it. When evaluating the models with holdout cross validation, the best model achieves an accuracy of 80% and an F-score of at least 0.7. Finally, we used the best model to prune partitions with large displacement in a circuit partitioning strategy. Experimental results in circuits (with up to millions of cells) showed that the pruning strategy improved the maximum displacement of the legalized solution by 5% to 94%. In addition, using the machine learning model avoided from 22% to 99% of the calls to the legalization algorithm, which speeds up the pruning process by up to 3×.

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