A Procedure for Improving the Distribution of Congestion in Global Routing

Shi, Daohang; Davoodi, Azadeh; Linderoth, Jeff

doi:10.3850/9783981537079_0732

Cited by 2 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A universal routing strategy is therefore greatly desirable [ 252 ]. Two-stage approaches, global routing followed by detailed routing, force-directed routing, region-wise routing, and rip-up and reroute [ 253 , 254 , 255 ], have been proposed to resolve complex large-scale circuit designs and to analyze track congestion at the global routing stage.…”

Section: Applications In Microelectronicsmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

View full text Add to dashboard Cite

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

show abstract

Section: Applications In Microelectronicsmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

View full text Add to dashboard Cite

show abstract

“…On the one hand, there are multiple heuristic-based approaches for global routing including regionwise routing (Hu and Sapatnekar, 2001), force-directed routing (Mo, Tabbara, and Brayton, 2001), and rip-up and reroute (Cho et al, 2007). Besides, (Shi, Davoodi, and Linderoth, 2016) proposes a method to improve the distribution of congestion in global routing, and (Shi and Davoodi, 2017) propose a framework to analyze local congestion for global routing. On the other hand, the most commonly used detailed routing algorithms are channel routing and its variants (Ho, Iyengar, and Zheng, 1991;Mandal et al, 2020), which decompose the routing region into routing channels and generate wires in these channels (Chen and Chang, 2009).…”

Section: Heuristic Algorithms For Circuit Rougingmentioning

confidence: 99%

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization

Huang,

Wang,

et al. 2021

Preprint

View full text Add to dashboard Cite

Circuit routing has been a historically challenging problem in designing electronic systems such as very large-scale integration (VLSI) and printed circuit boards (PCBs). The main challenge is that connecting a large number of electronic components under specific design rules involves a very large search space. Early solutions are typically designed with hard-coded heuristics, which suffer from problems of non-optimal solutions and lack of flexibility for new design needs. Although a few learning-based methods have been proposed recently, they are typically cumbersome and hard to extend to large-scale applications. In this work, we propose a new algorithm for circuit routing, named as Ranking Cost, which innovatively combines search-based methods (i.e., A* algorithm) and learningbased methods (i.e., Evolution Strategies) to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global objective. We also train a ranking parameter, which can produce the ranking order and further improve the performance of our method. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration. In the experiments, we compare with the sequential A* algorithm and a canonical reinforcement learning approach, and results show that our method outperforms these baselines with higher connectivity rates and better scalability.Preprint. Under review.

show abstract

A Procedure for Improving the Distribution of Congestion in Global Routing

Cited by 2 publications

References 13 publications

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization

Contact Info

Product

Resources

About