NSF Award
2324860
Edge devices like wearable watches and cameras, phones, and sensors in homes, factories, and farms, have become the foundation of our daily interactions with technology and the source of significant data for numerous critical sectors like health and manufacturing. Unfortunately, the exponential growth in sheer numbers of these devices comes at a significant environmental cost. User and edge devices alone account for over one-third of the 4% of global carbon emissions attributed to information and communication technologies. This ecological impact is projected to worsen as the number of edge devices surges into the trillions over the next few decades. This project develops Delphi, an end-to-end framework that prioritizes environmental impact while considering user experience, performance, and efficiency when designing edge devices. Delphi will enable sustainable technological growth by allowing for the design of environmentally conscious edge devices throughout their lifecycle. Via Delphi, software and hardware designers will be better able to reduce the potential environmental harms of computing while still offering valuable computational services for users and society. The project will lay the foundations for devices that can last for decades, potentially reducing e-waste and transient, short-lived devices designed to minimize environmental footprint. The project will integrate sustainability into college courses, provide broader online educational modules, and host a sustainability hackathon. Delphi tools themselves will be open-source, open hardware, and come with open datasets– all to facilitate further research and enable carbon-aware design to ensure a more sustainable future for computing.<br/> <br/>This project seeks to make carbon and sustainability a first-order design parameter for future edge computing devices that range from tiny, energy-harvesting Internet of Things (IoT) devices to higher-performance consumer electronics. Delphi is a suite of carbon-aware design tools that consider factors like energy, e-waste, and water usage from the manufacturing of computational devices, as well as operation carbon factors of machine learning and software lifecycles. The framework covers sensors, computing, communication, and power, accommodating various edge devices, and includes probabilistic analysis of machine learning workflows design and their operational and embodied carbon impact. The project's research has three major tasks. First, quantifying device environmental impact by collecting a first-of-its-kind dataset via a state-of-the-art academic clean room, the Cornell Nanoscale Facility (CNF), with architectural carbon models for salient device components (e.g., processor, memory and storage, energy harvesting modules). This task integrates data into new foundational carbon models, guiding all research tasks. Second, tools for the design of systems with sustainability as a first-order design target, alongside performance and quality of service. The task develops Electronic Sustainability Records for devices on the Pareto-frontier to maintain system-specific sustainability ledgers to track environmental telemetry across the operational lifetime of devices. Finally, the third task develops runtime sustainability managers, including humans in the loop, to reduce device obsolescence. The software will gracefully degrade and upgrade system performance based on user choices, static lifetime requirements, and environmental factors. The comprehensive framework's effectiveness is demonstrated through short-lived "Ephemeral devices" and lifelong companion health and wellness wearable devices, nicknamed the "Infinite Bit." These two device archetypes provide a mechanism for continuous validation as the project matures.<br/><br/>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.
