Summary of Beyond Efficiency: Scaling Ai Sustainably, by Carole-jean Wu et al.
Beyond Efficiency: Scaling AI Sustainably
by Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim Hazelwood
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper characterizes the carbon impact of AI, including operational emissions from training and inference as well as embodied emissions from datacenter construction and hardware manufacturing. It highlights efficiency optimization opportunities for cutting-edge AI technologies, such as deep learning recommendation models and multi-modal generative AI tasks. The authors emphasize the need to optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operations and end-of-life processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is a major contributor to carbon emissions due to its huge energy demands. Training and using AI models requires lots of electricity, which means more greenhouse gas emissions. The paper looks at how we can make AI more sustainable by reducing these emissions. It suggests ways to make AI more efficient, like optimizing algorithms and using recycled hardware. We need to think about the whole lifecycle of our computing infrastructure, from making new hardware to disposing of old equipment. |
Keywords
» Artificial intelligence » Deep learning » Inference » Multi modal » Optimization
