Collaborative Research: Conference: DESC: Type III: Eco Edge - Advancing Sustainable Machine Learning at the Edge

边缘计算设备和机器学习算法的激增有望改变技术基础设施，并实现医疗保健、制造业和智能城市等行业的实时分析。然而，到2030年，预计将有数百亿台边缘设备，因此必须研究其可持续性影响。本次研讨会汇集了来自工业界、学术界和政府的50多位专家，共同起草节能、环境可持续的边缘机器学习战略。通过特邀演讲和跨学科工作组，与会者将确定在评估和最大限度地减少边缘设备整个生命周期的碳足迹方面的挑战和机遇。研讨会将就优化模型设计、资源效率基准、可持续性政策激励等提出可操作的建议。通过采取涵盖技术、指标、工具和治理的整体方法，这一努力为边缘机器学习成为循环绿色经济的驱动力奠定了基础。 该研讨会促进了关于可持续边缘机器学习的前沿合作研究。技术工作组将研究提高能源效率、最大限度地减少电子废物以及减少边缘系统生命周期每个阶段对环境影响的方法。讨论将涉及设计用于全面影响评估的专业建模工具，创建逼真的场景以模拟长期影响，构建仿真平台以加速可持续设计选择，以及开发针对边缘机器学习的效率和碳足迹基准。工作组还将探索政策激励措施、负责任的边缘计算实践的环境标准以及碳排放以外的社会考虑因素，如生物圈完整性。该研讨会通过数据驱动的研究和多方利益相关者对话，为可持续创新制定可操作的战略，因为边缘智能改变了我们的生活和工作方式。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The proliferation of edge-computing devices and machine-learning algorithms promises to transform technology infrastructure and enable real-time analytics across sectors like healthcare, manufacturing, and smart cities. However, with tens of billions of edge devices expected by 2030, it is imperative to study their sustainability implications. This workshop brings together over 50 experts across industry, academia, and government to draft strategies for energy-efficient, environmentally-sustainable edge machine learning. Through invited talks and interdisciplinary working groups, participants will identify challenges and opportunities in assessing and minimizing the carbon footprint of edge devices throughout their lifecycle. The workshop will produce actionable recommendations on optimized model design, resource-efficiency benchmarks, policy incentives for sustainability, and more. By taking a holistic approach encompassing technology, metrics, tools, and governance, this effort lays the foundation to make edge machine learning a driver for a circular green economy. The workshop facilitates cutting-edge, collaborative research on sustainable edge machine learning. Technical working groups will investigate methods to improve energy efficiency, minimize electronic waste, and reduce the environmental impact at each stage of edge systems’ lifecycles. Discussions will address designing specialized modeling tools for comprehensive impact assessment, creating realistic scenarios to simulate long-term effects, building emulation platforms to accelerate sustainable design choices, and developing efficiency and carbon-footprint benchmarks tailored to edge machine learning. Workgroups will also explore policy incentives, environmental standards for responsible edge-computing practices, and societal considerations beyond carbon emissions, such as biosphere integrity. The workshop develops actionable strategies for sustainable innovation through data-driven studies and multi-stakeholder dialogue as edge intelligence transforms how we live and work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.