Carbon Connect: An Ecosystem for Sustainable Computing
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 22 May 2024.
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14 of 57 references.
References · 14
- 2022Emergent Abilities of Large Language Modelscited by 2,512
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
- 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 618
- 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
- 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
- 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
- 2020The Hardware Lotterycited by 160
- 2021Scaling Laws for Neural Machine Translationcited by 111
- 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
- 2020Chasing Carbon: The Elusive Environmental Footprint of Computingcited by 72
- 2020DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inferencecited by 54
- 2021Carbon-Aware Computing for Datacenterscited by 41
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