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

Harvard University, Cambridge MA

Investigators

Abstract

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.