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

• URL: http://arxiv.org/abs/2110.03939v1
• Date: Fri, 8 Oct 2021 07:22:45 GMT
• Title: Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization
• Authors: Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Ting Chen, Jun Zhu
• Abstract summary: We propose a new algorithm for circuit routing, named Ranking Cost, 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. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration.
• Score: 49.207538634692916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Ranking Cost, which innovatively combines search-based methods (i.e., A* algorithm) and learning-based 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.

Related papers

• CircuitVAE: Efficient and Scalable Latent Circuit Optimization [22.93567682576068]
  CircuitVAE is a search algorithm that embeds computation graphs in a continuous space. Our algorithm is highly sample-efficient, yet gracefully scales to large problem instances and high sample budgets. We find CircuitVAE can design state-of-the-art adders in a real-world chip, demonstrating that our method can outperform commercial tools in a realistic setting.
  arXiv  Detail & Related papers  (2024-06-13T18:47:52Z)
• An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem [67.92544792239086]
  We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics. First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
  arXiv  Detail & Related papers  (2024-03-11T02:17:42Z)
• Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems [20.684353068460375]
  When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. We propose a novel deep-learning-based approach called Genetic Algorithm with Neural Cost Predictor (GANCP) to tackle the challenge. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems.
  arXiv  Detail & Related papers  (2023-10-22T02:46:37Z)
• Inverse Preference Learning: Preference-based RL without a Reward Function [34.31087304327075]
  Inverse Preference Learning (IPL) is specifically designed for learning from offline preference data. Our key insight is that for a fixed policy, the $Q$-function encodes all information about the reward function, effectively making them interchangeable. IPL attains competitive performance compared to more complex approaches that leverage transformer-based and non-Markovian reward functions.
  arXiv  Detail & Related papers  (2023-05-24T17:14:10Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning [0.0]
  We introduce new auction-based algorithms for their optimal and suboptimal solution. The algorithms are based on mathematical ideas related to competitive bidding by persons for objects. The new algorithms have several potential advantages over existing methods.
  arXiv  Detail & Related papers  (2022-07-19T23:31:36Z)
• Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems [2.6782615615913348]
  This paper presents a new reinforcement learning approach that is based on entropy. In addition, we design an off-policy-based reinforcement learning technique that maximises the expected return. We show that our model can generalise to various route problems.
  arXiv  Detail & Related papers  (2022-05-31T09:51:48Z)
• DAAS: Differentiable Architecture and Augmentation Policy Search [107.53318939844422]
  This work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them. Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
  arXiv  Detail & Related papers  (2021-09-30T17:15:17Z)
• Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search [60.965024145243596]
  One-shot weight sharing methods have recently drawn great attention in neural architecture search due to high efficiency and competitive performance. To alleviate this problem, we present a simple yet effective architecture distillation method. We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training. Since the prioritized paths are changed on the fly depending on their performance and complexity, the final obtained paths are the cream of the crop.
  arXiv  Detail & Related papers  (2020-10-29T17:55:05Z)
• Physarum Powered Differentiable Linear Programming Layers and Applications [48.77235931652611]
  We propose an efficient and differentiable solver for general linear programming problems. We show the use of our solver in a video segmentation task and meta-learning for few-shot learning.
  arXiv  Detail & Related papers  (2020-04-30T01:50:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.