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

协作研究：会议：DESC：类型 III：生态边缘 - 推进边缘的可持续机器学习

• 批准号: 2342497
• 负责人: Vijay Janapa Reddi
• 金额: $ 5.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342497&HistoricalAwards=false
• 关键词: Collaborative; Research; Conference; DESC; Type; Collaborative; Research; Conference; DESC; Type

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.

边缘计算设备和机器学习算法的扩散有望改变技术基础架构，并在医疗保健，制造业和智能城市等领域实时分析。但是，到2030年预计有数数十亿个边缘设备，必须研究其可持续性的影响。该研讨会汇集了各个行业，学术界和政府的50多名专家，以起草节能，环保的边缘机器学习策略。通过邀请的会谈和跨学科工作组，参与者将确定在整个生命周期中评估和最大程度地评估边缘设备的碳足迹时的挑战和机遇。该研讨会将为优化的模型设计，资源效率基准，可持续性激励措施等提出可行的建议。通过采取整体方法，包括指标，工具和治理，这项工作奠定了基础，使Edge Machine学习成为循环绿色经济的驱动因素。研讨会设施剪裁，关于可持续边缘机器学习的协作研究。技术工作组将研究提高能源效率，最大程度减少电子废物并减少Edge System生命周期每个阶段的环境影响的方法。讨论将介绍设计专门的建模工具，以进行全面的影响评估，创建现实的场景以模拟长期效果，建立仿真平台以加速可持续的设计选择，并开发效率以及量身定制的，该基准量身定制为边缘机器学习。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，例如生物圈完整性。该研讨会通过数据驱动的研究制定了可持续创新的可行策略，随着边缘情报的方式改变了我们的生活和工作方式。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准通过评估来评估的。