用门控神经元选择自动设计小型高效通信网络模型
Automated Model Design using Gated Neuron Selection in Telecom
- 基于梯度的架构搜索方法，通过门控机制筛选神经元
- 性能提升同时模型缩小51%-82%，搜索速度加快36倍
- 适合需要快速部署轻量级AI的通信网络场景
电信行业正加速采用深度学习解决流量预测、信号强度预测和服务质量优化等关键任务。然而，在资源受限的网络环境中设计高性能且紧凑的神经网络架构仍具挑战性，耗时费力。为此，本文提出专为电信领域表格数据设计的新型梯度式神经架构搜索方法——TabGNS（Tabular Gated Neuron Selection）。我们在多个电信及通用表格数据集上评估了TabGNS，结果表明其在保持甚至提升预测性能的同时，将模型规模缩减51%-82%，搜索时间减少最多达36倍，优于当前最先进的表格型NAS方法。将TabGNS集成至模型生命周期管理中，可实现神经网络从设计到部署的全流程自动化，显著加速机器学习解决方案在电信网络中的落地。
原文摘要 · Abstract (English)
The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of service optimisation. However, designing neural network architectures for these applications remains challenging and time-consuming, particularly when targeting compact models suitable for resource-constrained network environments. Therefore, there is a need for automating the model design process to create high-performing models efficiently. This paper introduces TabGNS (Tabular Gated Neuron Selection), a novel gradient-based Neural Architecture Search (NAS) method specifically tailored for tabular data in telecommunications networks. We evaluate TabGNS across multiple telecommunications and generic tabular datasets, demonstrating improvements in prediction performance while reducing the architecture size by 51-82% and reducing the search time by up to 36x compared to state-of-the-art tabular NAS methods. Integrating TabGNS into the model lifecycle management enables automated design of neural networks throughout the lifecycle, accelerating deployment of ML solutions in telecommunications networks.
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