BonnRoute

Gester, Michael; Müller, Dirk; Nieberg, Tim; Panten, Christian; Schulte, Christian; Vygen, Jens

doi:10.1145/2442087.2442103

Cited by 34 publications

(5 citation statements)

References 58 publications

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“…[30] proposed an XSMT algorithm based on social learning DPSO and an effective twostage construction method to achieve the best wire length optimization effect. [31] presented an efficient VLSI routing algorithm employing novel DPSO and multistage transformation to build XSMT. [32] presented a unified algorithm for XSMT and RSMT construction based on a hybrid transformation strategy (HTS) and self-adapting DPSO.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

Yang

Liu

2023

Wireless Communications and Mobile Computing

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Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q -learning, the proposed global router adopts the Double Deep Q -Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

Yang

Liu

2023

Wireless Communications and Mobile Computing

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“…Routing to minimize the total number of vertices used is NP-hard, but polynomialtime approximation algorithms exist [56]. In practice, heuristic routing algorithms scale to problem sizes much larger than current QA architectures [57,58,59,60,61].…”

Section: Routingmentioning

confidence: 99%

“…This makes the problem harder, as the edge-weighted Steiner tree problem is (1.39)-approximable in polynomial time [62], while vertexweighted Steiner-tree is only (log k)-approximable for k terminals in polynomial time unless P=NP [63]. Nevertheless, in practice, simple 2-approximation algorithms for edge-weighted Steiner tree such as the MST algorithm [64] or Path Composition [65] also work very well for the vertex-weighted problem. In this section, we describe a modification of the routing algorithm BonnRoute [65] for vertex-weighted Steiner trees.…”

Section: Routingmentioning

confidence: 99%

“…Nevertheless, in practice, simple 2-approximation algorithms for edge-weighted Steiner tree such as the MST algorithm [64] or Path Composition [65] also work very well for the vertex-weighted problem. In this section, we describe a modification of the routing algorithm BonnRoute [65] for vertex-weighted Steiner trees.…”

Section: Routingmentioning

confidence: 99%

“…Algorithm 1 BonnRoute Resource Sharing Algorithm [65]. Require: Graph G, Steiner tree terminals {T 1 , .…”

Section: Routingmentioning

confidence: 99%

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Solving SAT and MaxSAT with a Quantum Annealer: Foundations and a Preliminary Report

Bian

Chudak

Macready

et al. 2017

Lecture Notes in Computer Science

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Quantum annealers (QAs) are specialized quantum computers that minimize objective functions over discrete variables by physically exploiting quantum effects. Current QA platforms allow for the optimization of quadratic objectives defined over binary variables (qubits), also known as Ising problems. In the last decade, QA systems as implemented by D-Wave have scaled with Moore-like growth. Current architectures provide 2048 sparsely-connected qubits, and continued exponential growth is anticipated, together with increased connectivity.We explore the feasibility of such architectures for solving SAT and MaxSAT problems as QA systems scale. We develop techniques for effectively encoding SAT -and, with some limitations, MaxSAT-into Ising problems compatible with sparse QA architectures. We provide the theoretical foundations for this mapping, and present encoding techniques that combine offline Satisfiability and Optimization Modulo Theories with on-the-fly placement and routing. Preliminary empirical tests on a current generation 2048-qubit D-Wave system support the feasibility of the approach for certain SAT and MaxSAT problems. 1 Superposition is perhaps the best-known and most surprising aspect of quantum physics (e.g. the famous Scrödinger's cat which is both dead and alive prior to observation).

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