[{"content":"Submission Deadline: May 15, 2026\nRecent advances in AI, especially large language and multimodal models, have been driven by rapid scaling of data, models, and compute—but at growing energy, resource, and environmental costs that few labs can afford. This workshop focuses on the sustainability of AI systems across their lifecycle, emphasizing data-, compute-, and energy-efficient methods, as well as quantitative, reproducible metrics and benchmarks. We invite contributions on methods, metrics, and real-world applications (including edge and embedded deployments) that enable AI systems to be both powerful and sustainable. Papers in this workshop must describe high-quality, original research.\nTopics of Interest Topics of interest cover all aspects of efficient and sustainable AI including, but not limited to:\nData- and label-efficient learning under resource constraints, including theoretical analyses and empirical studies Leveraging underutilized or unconventional data sources to reduce data and compute demands Contributions in high performance computing machine learning pipelines including energy-, resource-, hardware-, and time-efficient perception and inference Efficient multimodal AI, including but not limited to exploiting complementary modalities for efficiency (e.g., adaptive modality selection, sensor scheduling, shared representations) Sustainable re-use, adaptation, and life-cycle management of ML models, including fine-tuning of large foundation models, in-context and test-time learning, continual learning, and federated learning. Metrics, methodologies, and tools for measuring and evaluating efficiency and sustainability in ML (e.g., budgeted performance under energy, compute, or data constraints; carbon accounting; standardized reporting; benchmarks) Contributions in applications including design, deployment, and evaluation of efficient models on edge and mobile devices, for example case studies in industrial, agricultural, mobility, healthcare, and other real-world environments Sustainable hardware and materials, including algorithm–hardware co-design, low-power accelerators, and environmentally responsible hardware life-cycles Ethical, socio-economic, and socio-cultural aspects of sustainable AI, such as equity of access, environmental justice, governance, and policy implications Submit Your Paper Submission Tracks Full Papers (Archival, PMLR Proceedings) Original research papers of up to 7 pages for the main content, plus up to 2 additional pages for references (9 pages total). Accepted full papers will be published in a dedicated volume of the Proceedings of Machine Learning Research (PMLR).\nAcceptance Criteria: Novelty and relevance to efficient and sustainable AI, technical soundness and appropriate methodology (including evaluation), potential impact, as well as clarity and quality of presentation.\nShort Papers (Non-Archival) Short papers of up to 4 pages for the main content, plus 1 page for references (5 pages total). Short papers are intended for work-in-progress, promising preliminary results, and ideas that would benefit from feedback and discussion. Short papers will not appear in the archival proceedings, allowing authors to later submit extended versions to other conferences or journals.\nAcceptance Criteria: Clarity and plausibility of the core idea or hypothesis, methodological soundness of first results or proof of concept, potential for impact and stimulating discussion at the workshop.\nPosition Papers (Non-Archival) Position papers of up to 2 pages plus 1 page for references (3 pages total). Contributions may articulate research visions, identify open challenges, propose benchmarks or standards, or offer critical perspectives on efficient and sustainable AI. Position papers are non-archival and are primarily meant to serve as a basis for discussions and interactive sessions at the workshop.\nAcceptance Criteria: Originality and thought-provoking perspectives, a well-argued and clearly articulated position grounded in relevant literature or practice, potential to catalyze discussion.\nAll submissions must follow the official SuRE workshop formatting guidelines and will undergo double-blind peer review. Download the author kit for templates and style files: On GitHub · Download as ZIP\nImportant Dates All deadlines are Anywhere on Earth (AoE). Milestone Date 📄 Submission deadline (all tracks) May 15, 2026 📬 Notification of acceptance June 1, 2026 📝 Camera-ready deadline June 15, 2026 🗓️ Workshop day August 2026 (TBD) Contact In case of questions, contact us at: ✉️ sure-organizers@dfki.de\n","permalink":"https://sure-wshop.github.io/cfp/","summary":"\u003cp\u003e\u003cspan class=\"workshop-badge\"\u003eSubmission Deadline: May 15, 2026\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eRecent advances in AI, especially large language and multimodal models, have been driven by rapid scaling of data, models, and compute—but at growing energy, resource, and environmental costs that few labs can afford.\nThis workshop focuses on the sustainability \u003cem\u003eof\u003c/em\u003e AI systems across their lifecycle, emphasizing data-, compute-, and energy-efficient methods, as well as quantitative, reproducible metrics and benchmarks.\nWe invite contributions on methods, metrics, and real-world applications (including edge and embedded deployments) that enable AI systems to be both powerful and sustainable.\nPapers in this workshop must describe high-quality, original research.\u003c/p\u003e","title":"Call for Papers"},{"content":" Prof. Dr. Paul Lukowicz German Research Center for Artificial Intelligence (DFKI)\nResearch Interests: Embedded Systems, Wearable and Ubiquitous Computing\nTitle: TBA\nSpeaker TBD To be announced. We are currently in discussion with leading researchers in sustainable AI.\n","permalink":"https://sure-wshop.github.io/speakers/","summary":"\u003chr\u003e\n\u003ch3 id=\"prof-dr-paul-lukowicz\"\u003eProf. Dr. Paul Lukowicz\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eGerman Research Center for Artificial Intelligence (DFKI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResearch Interests:\u003c/em\u003e Embedded Systems, Wearable and Ubiquitous Computing\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTitle: TBA\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch3 id=\"speaker-tbd\"\u003eSpeaker TBD\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTo be announced. We are currently in discussion with leading researchers in sustainable AI.\u003c/p\u003e\n\u003c/blockquote\u003e","title":"Invited Speakers"},{"content":"Contact us at: ✉️ sure-organizers@dfki.de\nVitor Fortes Rey DFKI \u0026 RPTU University Kaiserslautern-Landau Wearable Human Activity Recognition · TinyML 🎓 Scholar René Schuster DFKI \u0026 RPTU University Kaiserslautern-Landau Continual Learning · Neuromorphic Vision 🌐 Website 🎓 Scholar Niklas Baumgarten Heidelberg University, Dept. of Mathematics Stochastic Optimization · Hardware-Aware Distributed Computing 🌐 Website 🎓 Scholar Sungho Suh Korea University Wearable Computing · Vision-Language-Action Models 🌐 Website 🎓 Scholar Tobias Christian Nauen DFKI \u0026 RPTU University Kaiserslautern-Landau Measuring Efficiency in CV · Data-Efficient Training 🌐 Website 🎓 Scholar Affiliated Institutions ","permalink":"https://sure-wshop.github.io/organizers/","summary":"\u003cp\u003eContact us at: \u003ca href=\"mailto:sure-organizers@dfki.de\"\u003e✉️ sure-organizers@dfki.de\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"organizer-grid\"\u003e\n  \u003cdiv class=\"organizer-card\"\u003e\n    \u003cimg class=\"organizer-photo\" src=\"/img/organizers/vitor.jpg\" alt=\"Vitor Fortes Rey\"\u003e\n    \u003ch3\u003eVitor Fortes Rey\u003c/h3\u003e\n    \u003cdiv class=\"affiliation\"\u003eDFKI \u0026 RPTU University Kaiserslautern-Landau\u003c/div\u003e\n    \u003cdiv class=\"interests\"\u003eWearable Human Activity Recognition · TinyML\u003c/div\u003e\n    \u003cdiv class=\"organizer-links\"\u003e\n      \u003ca href=\"https://scholar.google.co.uk/citations?hl=de\u0026user=rjJwgO0AAAAJ\" target=\"_blank\"\u003e🎓 Scholar\u003c/a\u003e\n    \u003c/div\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"organizer-card\"\u003e\n    \u003cimg class=\"organizer-photo\" src=\"/img/organizers/rene.jpg\" alt=\"René Schuster\"\u003e\n    \u003ch3\u003eRené Schuster\u003c/h3\u003e\n    \u003cdiv class=\"affiliation\"\u003eDFKI \u0026 RPTU University Kaiserslautern-Landau\u003c/div\u003e\n    \u003cdiv class=\"interests\"\u003eContinual Learning · Neuromorphic Vision\u003c/div\u003e\n    \u003cdiv class=\"organizer-links\"\u003e\n      \u003ca href=\"https://av.dfki.de/members/schuster/\" target=\"_blank\"\u003e🌐 Website\u003c/a\u003e\n      \u003ca href=\"https://scholar.google.co.uk/citations?hl=de\u0026user=-QuwLyTNzvAC\" target=\"_blank\"\u003e🎓 Scholar\u003c/a\u003e\n    \u003c/div\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"organizer-card\"\u003e\n    \u003cimg class=\"organizer-photo\" src=\"/img/organizers/niklas.jpg\" alt=\"Niklas Baumgarten\"\u003e\n    \u003ch3\u003eNiklas Baumgarten\u003c/h3\u003e\n    \u003cdiv class=\"affiliation\"\u003eHeidelberg University, Dept. of Mathematics\u003c/div\u003e\n    \u003cdiv class=\"interests\"\u003eStochastic Optimization · Hardware-Aware Distributed Computing\u003c/div\u003e\n    \u003cdiv class=\"organizer-links\"\u003e\n      \u003ca href=\"https://publish.obsidian.md/niklas-baumgarten/webpage/about\" target=\"_blank\"\u003e🌐 Website\u003c/a\u003e\n      \u003ca href=\"https://scholar.google.co.uk/citations?hl=de\u0026user=NWhNzMsAAAAJ\" target=\"_blank\"\u003e🎓 Scholar\u003c/a\u003e\n    \u003c/div\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"organizer-card\"\u003e\n    \u003cimg class=\"organizer-photo\" src=\"/img/organizers/sungho.jpg\" alt=\"Sungho Suh\"\u003e\n    \u003ch3\u003eSungho Suh\u003c/h3\u003e\n    \u003cdiv class=\"affiliation\"\u003eKorea University\u003c/div\u003e\n    \u003cdiv class=\"interests\"\u003eWearable Computing · Vision-Language-Action Models\u003c/div\u003e\n    \u003cdiv class=\"organizer-links\"\u003e\n      \u003ca href=\"https://sites.google.com/view/sunghosuh/home\" target=\"_blank\"\u003e🌐 Website\u003c/a\u003e\n      \u003ca href=\"https://scholar.google.co.uk/citations?hl=de\u0026user=DMjDOYwAAAAJ\" target=\"_blank\"\u003e🎓 Scholar\u003c/a\u003e\n    \u003c/div\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"organizer-card\"\u003e\n    \u003cimg class=\"organizer-photo\" src=\"/img/organizers/tobias.jpg\" alt=\"Tobias Christian Nauen\"\u003e\n    \u003ch3\u003eTobias Christian Nauen\u003c/h3\u003e\n    \u003cdiv class=\"affiliation\"\u003eDFKI \u0026 RPTU University Kaiserslautern-Landau\u003c/div\u003e\n    \u003cdiv class=\"interests\"\u003eMeasuring Efficiency in CV · Data-Efficient Training\u003c/div\u003e\n    \u003cdiv class=\"organizer-links\"\u003e\n      \u003ca href=\"https://nauen-it.de\" target=\"_blank\"\u003e🌐 Website\u003c/a\u003e\n      \u003ca href=\"https://scholar.google.co.uk/citations?user=NNVtvwYAAAAJ\" target=\"_blank\"\u003e🎓 Scholar\u003c/a\u003e\n    \u003c/div\u003e\n  \u003c/div\u003e\n\u003c/div\u003e\n\u003chr\u003e\n\u003ch2 id=\"affiliated-institutions\"\u003eAffiliated Institutions\u003c/h2\u003e\n\u003cdiv class=\"funder-strip\"\u003e\n  \u003ca href=\"https://www.dfki.de/\" target=\"_blank\"\u003e\u003cimg src=\"/img/dfki.png\" alt=\"DFKI\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://rptu.de/\" target=\"_blank\"\u003e\u003cimg src=\"/img/rptu.png\" alt=\"RPTU\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.uni-heidelberg.de/\" target=\"_blank\"\u003e\u003cimg src=\"/img/heidelberg.png\" alt=\"Heidelberg University\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.korea.ac.kr/\" target=\"_blank\"\u003e\u003cimg src=\"/img/korea-uni.png\" alt=\"Korea University\"\u003e\u003c/a\u003e\n\u003c/div\u003e","title":"Organizing Committee"},{"content":"The workshop is a half-day event combining invited keynotes, contributed paper presentations, and interactive discussion rounds.\nTimeSession ~30 min Keynote I\nProf. Dr. Paul Lukowicz — DFKI, Germany\nTitle TBD ~90 min Paper Presentations — Session I ~30 min Position Paper Discussion Round\nFloating group discussions per position paper topic, followed by brief summaries ~30 min Keynote II\nSpeaker TBD\nTitle TBD ~10 min Best Paper Award \u0026 Closing Remarks ","permalink":"https://sure-wshop.github.io/schedule/","summary":"\u003cp\u003eThe workshop is a \u003cstrong\u003ehalf-day event\u003c/strong\u003e combining invited keynotes, contributed paper presentations,\nand interactive discussion rounds.\u003c/p\u003e\n\u003ctable class=\"schedule-table\"\u003e\n  \u003cthead\u003e\n    \u003ctr\u003e\u003cth\u003eTime\u003c/th\u003e\u003cth\u003eSession\u003c/th\u003e\u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003ctd class=\"time-col\"\u003e~30 min\u003c/td\u003e\n      \u003ctd\u003e\n        \u003cstrong\u003eKeynote I\u003c/strong\u003e\u003cbr\u003e\n        Prof. Dr. Paul Lukowicz — DFKI, Germany\u003cbr\u003e\n        \u003cem\u003eTitle TBD\u003c/em\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd class=\"time-col\"\u003e~90 min\u003c/td\u003e\n      \u003ctd\u003e\u003cstrong\u003ePaper Presentations — Session I\u003c/strong\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd class=\"time-col\"\u003e~30 min\u003c/td\u003e\n      \u003ctd\u003e\n        \u003cstrong\u003ePosition Paper Discussion Round\u003c/strong\u003e\u003cbr\u003e\n        \u003cem\u003eFloating group discussions per position paper topic, followed by brief summaries\u003c/em\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd class=\"time-col\"\u003e~30 min\u003c/td\u003e\n      \u003ctd\u003e\n        \u003cstrong\u003eKeynote II\u003c/strong\u003e\u003cbr\u003e\n        Speaker TBD\u003cbr\u003e\n        \u003cem\u003eTitle TBD\u003c/em\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd class=\"time-col\"\u003e~10 min\u003c/td\u003e\n      \u003ctd\u003e\u003cstrong\u003eBest Paper Award \u0026 Closing Remarks\u003c/strong\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e","title":"Preliminary Program \u0026 Schedule"},{"content":"We thank the following researchers for serving on the program committee:\nName Affiliation Expertise Ramy Battrawy DFKI Efficient Fusion and Attention Katharina Bending RPTU Event-based Vision, Sparse Networks, Spiking Neural Networks Ettore Barbagallo RPTU Ethics of Technology, AI Ethics Stanislav Frolov DFKI Multimodal AI, Data-Efficiency, Generative AI, Continual Learning Daniel Geißler DFKI \u0026amp; RPTU Sustainable ML, Latent Space Optimization Vinit Vikas Hegiste RPTU Federated ML, AI in Manufacturing Rahul Jakkamsetty RPTU Sensor Fusion Jakob Karolus DFKI \u0026amp; RPTU HCI, Human-Centered AI Sarah Keren Technion Model-based Reasoning, Decision-making under Uncertainty Lars Krupp DFKI \u0026amp; RPTU LLM Energy Estimation, Agent Energy Benchmarking Tatjana Legler RPTU Federated ML, AI in Manufacturing Paul Lukowicz DFKI Embedded Systems, Wearable and Ubiquitous Computing Vishal Sharbidra Mukunda RPTU AI in Agriculture Shishir Muralidhara DFKI Incremental Learning, Segmentation Vladimir Rybalkin RPTU Edge/Mobile Deployment, Algorithm-Hardware Co-design Aheli Saha RPTU Neuromorphic Computing ","permalink":"https://sure-wshop.github.io/program-committee/","summary":"\u003cp\u003eWe thank the following researchers for serving on the program committee:\u003c/p\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth\u003eName\u003c/th\u003e\n          \u003cth\u003eAffiliation\u003c/th\u003e\n          \u003cth\u003eExpertise\u003c/th\u003e\n      \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eRamy Battrawy\u003c/td\u003e\n          \u003ctd\u003eDFKI\u003c/td\u003e\n          \u003ctd\u003eEfficient Fusion and Attention\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eKatharina Bending\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eEvent-based Vision, Sparse Networks, Spiking Neural Networks\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eEttore Barbagallo\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eEthics of Technology, AI Ethics\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eStanislav Frolov\u003c/td\u003e\n          \u003ctd\u003eDFKI\u003c/td\u003e\n          \u003ctd\u003eMultimodal AI, Data-Efficiency, Generative AI, Continual Learning\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eDaniel Geißler\u003c/td\u003e\n          \u003ctd\u003eDFKI \u0026amp; RPTU\u003c/td\u003e\n          \u003ctd\u003eSustainable ML, Latent Space Optimization\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eVinit Vikas Hegiste\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eFederated ML, AI in Manufacturing\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eRahul Jakkamsetty\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eSensor Fusion\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eJakob Karolus\u003c/td\u003e\n          \u003ctd\u003eDFKI \u0026amp; RPTU\u003c/td\u003e\n          \u003ctd\u003eHCI, Human-Centered AI\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eSarah Keren\u003c/td\u003e\n          \u003ctd\u003eTechnion\u003c/td\u003e\n          \u003ctd\u003eModel-based Reasoning, Decision-making under Uncertainty\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eLars Krupp\u003c/td\u003e\n          \u003ctd\u003eDFKI \u0026amp; RPTU\u003c/td\u003e\n          \u003ctd\u003eLLM Energy Estimation, Agent Energy Benchmarking\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eTatjana Legler\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eFederated ML, AI in Manufacturing\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003ePaul Lukowicz\u003c/td\u003e\n          \u003ctd\u003eDFKI\u003c/td\u003e\n          \u003ctd\u003eEmbedded Systems, Wearable and Ubiquitous Computing\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eVishal Sharbidra Mukunda\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eAI in Agriculture\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eShishir Muralidhara\u003c/td\u003e\n          \u003ctd\u003eDFKI\u003c/td\u003e\n          \u003ctd\u003eIncremental Learning, Segmentation\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eVladimir Rybalkin\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eEdge/Mobile Deployment, Algorithm-Hardware Co-design\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd\u003eAheli Saha\u003c/td\u003e\n          \u003ctd\u003eRPTU\u003c/td\u003e\n          \u003ctd\u003eNeuromorphic Computing\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e","title":"Program Committee"}]