{"id":26201,"date":"2022-11-06T06:00:00","date_gmt":"2022-11-06T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26201"},"modified":"2022-11-03T12:47:11","modified_gmt":"2022-11-03T19:47:11","slug":"the-move-toward-green-machine-learning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/11\/06\/the-move-toward-green-machine-learning\/","title":{"rendered":"The Move Toward Green Machine Learning"},"content":{"rendered":"\n<p>A new study suggests tactics for machine learning engineers to cut their carbon emissions.\u00a0Led by David Patterson, researchers at Google and UC Berkeley <a href=\"https:\/\/arxiv.org\/abs\/2104.10350\" target=\"_blank\" rel=\"noreferrer noopener\">found<\/a> that AI developers can shrink a model\u2019s carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers.\u00a0<\/p>\n\n\n\n<p>The authors examined the total energy used and carbon emitted by five NLP models: <a href=\"https:\/\/arxiv.org\/abs\/2005.14165\" target=\"_blank\" rel=\"noreferrer noopener\">GPT-3<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2006.16668\" target=\"_blank\" rel=\"noreferrer noopener\">GShard<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2001.09977\" target=\"_blank\" rel=\"noreferrer noopener\">Meena<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2101.03961\" target=\"_blank\" rel=\"noreferrer noopener\">Switch Transformer<\/a>, and <a href=\"https:\/\/www.jmlr.org\/papers\/volume21\/20-074\/20-074.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">T5<\/a>. They reported separate figures for training and inference. Generally, they found that inference consumes more energy than training:<\/p>\n\n\n\n<ul><li>The authors point to several model-design strategies that trim energy use. Transfer learning, for instance, eliminates the need to train new models from scratch. Shrinking networks through techniques such as pruning and distillation can increase energy efficiency by a factor of 3 to 7.\u00a0<\/li><li>Hardware makes a difference, too. Chips designed specifically for machine learning are both faster and more efficient than GPUs. For instance, a Google TPU v2 ran a <a href=\"https:\/\/arxiv.org\/abs\/1706.03762\" target=\"_blank\" rel=\"noreferrer noopener\">transformer<\/a> 4.3 times faster and used 1.3 times less energy than an Nvidia P100.\u00a0<\/li><li>Cloud computing centers with servers optimized for machine learning are twice as efficient as traditional enterprise data centers. Data centers using renewable energy sources are greener, and centers built near their energy source bring further savings, as transmitting energy over long distances is relatively expensive and inefficient.\u00a0<\/li><\/ul>\n\n\n\n<p>The authors joined the <a href=\"https:\/\/arxiv.org\/abs\/1907.10597\" target=\"_blank\" rel=\"noreferrer noopener\">Allen Institute<\/a> and <a href=\"https:\/\/spectrum.ieee.org\/energy-efficient-green-ai-strategies#toggle-gdpr\" target=\"_blank\" rel=\"noreferrer noopener\">others<\/a> in calling for greener AI. To this end, MLCommons, the organization behind the MLPerf benchmark, recently introduced <a href=\"https:\/\/mlcommons.org\/en\/news\/mlperf-inference-v10\/\" target=\"_blank\" rel=\"noreferrer noopener\">new tools<\/a> to measure a model\u2019s energy consumption alongside traditional performance metrics.<\/p>\n\n\n\n<p>Training and deploying a large model can emit five times more carbon dioxide than a single car over the course of its lifetime. As AI becomes more widespread, energy efficiency becomes ever more important. There are bigger levers for reducing carbon emissions, such as transitioning the world away from coal power. Still, as a leading-edge industry, AI has an important role in building a the green future.<\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn:&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook:&nbsp;<a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new study suggests tactics for machine learning engineers to cut their carbon emissions.\u00a0Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model\u2019s carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers.\u00a0<\/p>\n","protected":false},"author":37,"featured_media":30803,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,180,122,67,268,56,84,1],"tags":[437,280,264,947,1227,277,635,1131],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/11\/weather_prediction_shutterstock_1286944201.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-6OB","jetpack-related-posts":[{"id":32878,"url":"https:\/\/insidebigdata.com\/2023\/07\/25\/video-highlights-generative-ai-with-large-language-models\/","url_meta":{"origin":26201,"position":0},"title":"Video Highlights: Generative AI with Large Language Models","date":"July 25, 2023","format":false,"excerpt":"At an unprecedented pace, Large Language Models like GPT-4 are transforming the world in general and the field of data science in particular. 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