{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/irene-viz-visualizing-energy-consumption-of","title":"IrEne-viz: Visualizing Energy Consumption of Transformer Models","arxiv_id":null,"date":"2021-11-01","proceeding":"EMNLP (ACL) 2021 11","authors":["Yash Kumar Lal","Reetu Singh","Harsh Trivedi","Qingqing Cao","Aruna Balasubramanian","Niranjan Balasubramanian"],"abstract":"IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models. We present the IrEne-viz tool, an online platform for visualizing and exploring energy consumption of various Transformer-based models easily. Additionally, we release a public API that can be used to access granular information about energy consumption of transformer models and their components. The live demo is available at http://stonybrooknlp.github.io/irene/demo/.","url_abs":"https://aclanthology.org/2021.emnlp-demo.29","url_pdf":"https://aclanthology.org/2021.emnlp-demo.29.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"irene-viz-visualizing-energy-consumption-of","repo_url":"https://github.com/LiamMaclean216/Pytorch-Transfomer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}