Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This work addresses this challenge by proposing a graph attention network method with customization, called Net2, to estimate individual net length before cell placement. Its accuracy-oriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than 1000 times faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.
The impact of sensor placement on graph-neural-network-based leakage detection
Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学控制系统组）；Wetsus, Centre of Excellence for Sustainable Water Technology（可持续水技术卓越中心）；Statistical Methods – Biometris, Wageningen University（瓦格宁根大学统计方法—生物计量学）
CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum
atlanTTic - I&C Lab - Universidade de Vigo（atlanTTic - I&C实验室 - 维戈大学）；Centro Tecnolóxico de Telecomunicacións de Galicia (GRADIANT)（加利西亚电信技术中心（GRADIANT））
Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
Harokopio University of Athens（雅典哈罗科皮奥大学）；National Technical University of Athens（雅典国家技术大学）；Institute of Information Science and Technologies, National Research Council (CNR)（国家研究委员会信息科学与技术研究所）；University of Pisa（比萨大学）
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
Microsoft Research Asia（微软亚洲研究院）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Shanghai Jiao Tong University（上海交通大学）
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption
Universidade Federal de Pernambuco (UFPE)（伯南布哥联邦大学（UFPE））；Dublin City University (DCU)（都柏林城市大学（DCU））；Universidade de Pernambuco (UPE)（伯南布哥大学（UPE））
软件定义网络（SDN）和网络功能虚拟化（NFV）正在使网络可编程，从而更加灵活和敏捷。为了满足服务水平协议，实现传统网络的更大利用率，更快的服务部署并减少支出，电信运营商正在部署越来越复杂的服务功能链（SFC）。尽管SFC具有诸多优势，但从云到边缘的异构性和动态性的增加带来了重大的SFC放置挑战，尤其是在保持可用性、服务质量和最小化成本的同时添加或删除网络功能。本文提出了一种基于强化学习（RL）的可用性和能量感知的动态SFC布局解决方案。两个策略感知RL算法，优势演员批评（A2C）和接近的政策优化（PPO2），使用模拟的地面实况网络拓扑结构的基础上，Rede Nacional de Ensino e Pesquisa（RNP）网络，巴西的国家教学和研究网络骨干。仿真结果表明，PPO2一般优于A2C和贪婪的方法，无论是在接受率和能源消耗。A2C仅在网络服务器拥有更多计算资源的情况下才优于PPO2。
Forecasting Faculty Placement from Patterns in Co-authorship Networks
Khoury College of Computer Sciences, Northeastern University（诺思韦普大学科里学院）；Network Science Institute, Northeastern University（网络科学研究所）；Santa Fe Institute（圣菲研究所）
