Carbon Connect: An Ecosystem for Sustainable Computing
<|reference_start|>Carbon Connect: An Ecosystem for Sustainable Computing: Computing is at a moment of profound opportunity. Emerging applications -- such as capable artificial intelligence, immersive virtual realities, and pervasive sensor systems -- drive unprecedented demand for computer. Despite recent advances toward net zero carbon emissions, the computing industry's gross energy usage continues to rise at an alarming rate, outpacing the growth of new energy installations and renewable energy deployments. A shift towards sustainability is needed to spark a transformation in how computer systems are manufactured, allocated, and consumed. Carbon Connect envisions coordinated research thrusts that produce design and management strategies for sustainable, next-generation computer systems. These strategies must flatten and then reverse growth trajectories for computing power and carbon for society's most rapidly growing applications such as artificial intelligence and virtual spaces. We will require accurate models for carbon accounting in computing technology. For embodied carbon, we must re-think conventional design strategies -- over-provisioned monolithic servers, frequent hardware refresh cycles, custom silicon -- and adopt life-cycle design strategies that more effectively reduce, reuse and recycle hardware at scale. For operational carbon, we must not only embrace renewable energy but also design systems to use that energy more efficiently. Finally, new hardware design and management strategies must be cognizant of economic policy and regulatory landscape, aligning private initiatives with societal goals. Many of these broader goals will require computer scientists to develop deep, enduring collaborations with researchers in economics, law, and industrial ecology to spark change in broader practice.<|reference_end|>
@article{lee2024carbon,
    title={Carbon Connect: An Ecosystem for Sustainable Computing},
    author={Benjamin C. Lee, David Brooks, Arthur van Benthem, Udit Gupta, Gage
  Hills, Vincent Liu, Benjamin Pierce, Christopher Stewart, Emma Strubell,
  Gu-Yeon Wei, Adam Wierman, Yuan Yao, Minlan Yu},
    journal={arXiv preprint arXiv:2405.13858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13858},
    primaryClass={cs.DC cs.AR cs.ET cs.LG}
}