Summary of Toward Cross-layer Energy Optimizations in Ai Systems, by Jae-won Chung and Nishil Talati and Mosharaf Chowdhury
Toward Cross-Layer Energy Optimizations in AI Systems
by Jae-Won Chung, Nishil Talati, Mosharaf Chowdhury
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 “AI for Science, Energy, and Security” report from DOE highlights the significance of optimizing artificial intelligence workflows to impact various missions. As AI and machine learning tools become more widespread, their energy efficiency is crucial due to massive energy consumption by generative AI models. For instance, training a 200-billion parameter large language model at Amazon consumed 11.9 GWh, equivalent to powering over a thousand average U.S. households for a year. Inference also consumes significant energy as trained models serve millions. The report emphasizes the importance of high energy efficiency in addressing power delivery issues for constructing and operating AI-specialized supercomputers and datacenters. It outlines software- and architecture-level research challenges and opportunities to create cross-layer energy optimizations in AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new report from DOE talks about using artificial intelligence (AI) to help with science, energy, and security tasks. Right now, many AI tools use a lot of energy, which can be a problem when building big supercomputers and datacenters that handle these tasks. For example, training one kind of AI model at Amazon used as much energy as powering over 1,000 average U.S. homes for a year! The report says we need to make AI more efficient so it doesn’t use up too much energy and cause problems. |
Keywords
» Artificial intelligence » Inference » Large language model » Machine learning
