GANGR: GAN-Assisted Scalable and Efficient Global Routing Parallelization
- URL: http://arxiv.org/abs/2511.17665v1
- Date: Fri, 21 Nov 2025 00:32:33 GMT
- Title: GANGR: GAN-Assisted Scalable and Efficient Global Routing Parallelization
- Authors: Hadi Khodaei Jooshin, Inna Partin-Vaisband,
- Abstract summary: Global routing is a critical stage in electronic design automation (EDA)<n>This paper introduces Wasserstein generative networks (WGANs) to enable more effective parallelization.<n>The proposed algorithm is tested on the latest ISPD'24 contest benchmarks, demonstrating up to 40% reduction with only 0.002% degradation in routing quality as compared to state-of-the-art routers.
- Score: 0.6117371161379208
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Global routing is a critical stage in electronic design automation (EDA) that enables early estimation and optimization of the routability of modern integrated circuits with respect to congestion, power dissipation, and design complexity. Batching is a primary concern in top-performing global routers, grouping nets into manageable sets to enable parallel processing and efficient resource usage. This process improves memory usage, scalable parallelization on modern hardware, and routing congestion by controlling net interactions within each batch. However, conventional batching methods typically depend on heuristics that are computationally expensive and can lead to suboptimal results (oversized batches with conflicting nets, excessive batch counts degrading parallelization, and longer batch generation times), ultimately limiting scalability and efficiency. To address these limitations, a novel batching algorithm enhanced with Wasserstein generative adversarial networks (WGANs) is introduced in this paper, enabling more effective parallelization by generating fewer higher-quality batches in less time. The proposed algorithm is tested on the latest ISPD'24 contest benchmarks, demonstrating up to 40% runtime reduction with only 0.002% degradation in routing quality as compared to state-of-the-art router.
Related papers
- Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
arXiv Detail & Related papers (2026-02-27T04:30:35Z) - Implementation of high-efficiency, lightweight residual spiking neural network processor based on field-programmable gate arrays [0.49806798459446283]
This work presents an efficient residual SNN accelerator that combines algorithm and hardware co-design to optimize inference energy efficiency.<n>The proposed processor achieves a classification accuracy of 87.11% on the CIFAR-10 dataset, with an inference time of 3.98 ms per image and an energy efficiency of 183.5 FPS/W.
arXiv Detail & Related papers (2025-12-09T02:08:46Z) - Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs [61.953548065938385]
We introduce the ''Three Taxes'' (Bulk Synchronous, Inter- Kernel Data Locality, and Kernel Launch Overhead) as an analytical framework.<n>We propose moving beyond the rigid BSP model to address key inefficiencies in distributed GPU execution.<n>We observe a 10-20% speedup in end-to-end latency over BSP-based approaches.
arXiv Detail & Related papers (2025-11-04T01:15:44Z) - Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing [53.88774113545582]
This paper presents a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework.<n>It aims to minimize the system cost, and thus realize an improved trade-off between task consumption and energy consumption.<n>We show that the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task consumption and energy consumption.
arXiv Detail & Related papers (2025-10-23T02:55:40Z) - Swap Network Augmented Ansätze on Arbitrary Connectivity [0.0]
We introduce an algorithm that optimize qubit routing for arbitrary connectivity graphs, resulting in a swap network that enables direct interactions between any pair of qubits.<n>We then propose a co-design of circuit layers and qubit routing by embedding the derived swap networks within layered, connectivity-aware ans"atze.<n>This construction significantly improves the trainability of the ansatz, leading to enhanced performance with reduced resources.
arXiv Detail & Related papers (2025-07-31T15:56:28Z) - AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search [14.46041554295883]
This paper introduces a solution that integrates Monte Carlo Tree Search (MCTS) with Reinforcement Learning (RL)
Our router, called Alpha RL, outperforms the current state-of-the-art routing methods and generates quantum programs with up to $20%$ less routing overhead.
arXiv Detail & Related papers (2024-10-07T15:10:54Z) - Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics [29.49913315698914]
Deep Reinforcement Learning (DRL) has emerged as a promising approach for handling highly dynamic and nonlinear Active Flow Control (AFC) problems.
This study focuses on optimizing DRL-based algorithms in parallel settings.
We achieve a significant boost in parallel efficiency from around 49% to approximately 78%.
arXiv Detail & Related papers (2024-02-18T09:07:30Z) - Robust Path Selection in Software-defined WANs using Deep Reinforcement
Learning [18.586260468459386]
We propose a data-driven algorithm that does the path selection in the network considering the overhead of route computation and path updates.
Our scheme fares well by a factor of 40% with respect to reducing link utilization compared to traditional TE schemes such as ECMP.
arXiv Detail & Related papers (2022-12-21T16:08:47Z) - Teal: Learning-Accelerated Optimization of WAN Traffic Engineering [68.7863363109948]
We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control.
To reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand.
Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625x speedups.
arXiv Detail & Related papers (2022-10-25T04:46:30Z) - Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
Entanglement routing establishes remote entanglement connection between two arbitrary nodes.
We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
arXiv Detail & Related papers (2021-11-15T14:07:22Z) - Fast and Complete: Enabling Complete Neural Network Verification with
Rapid and Massively Parallel Incomplete Verifiers [112.23981192818721]
We propose to use backward mode linear relaxation based analysis (LiRPA) to replace Linear Programming (LP) during the BaB process.
Unlike LP, LiRPA when applied naively can produce much weaker bounds and even cannot check certain conflicts of sub-domains during splitting.
We demonstrate an order of magnitude speedup compared to existing LP-based approaches.
arXiv Detail & Related papers (2020-11-27T16:42:12Z)
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.