BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:tinyML: Deep Learning Models on Low-power Micro-Controllers DTSTART:20260513T073000Z DTEND:20260513T103000Z DTSTAMP:20260502T155600Z UID:indico-event-287@events.asc.ac.at CONTACT:training@ai-at.eu DESCRIPTION:Start Date: 13 May 2026\, 09:30 (CET)   Entry level:  Adva ncedEnd date:  13 May 2026\, 12:30 (CET)   Subject area:  Artificial I ntelligence (AI)Location:  HYBRID: ONLINE / LIVE AI Factory Austria AI:A T\, Karl-Farkas-Gasse 22\, 1030 Wien    Topics:  tinyMLLanguage:  En glish     Target audience:  Embedded Software DevelopersPrice:  Free (for eligible participants)     Organizers:  AI:AT Discover how TinyM L brings deep learning directly onto microcontrollers and sensors. This tu torial guides embedded developers through the full pipeline\, from data pr ep and lightweight model design to evaluation and code generation with TVM \, enabling intelligent\, low-power edge applications.Intelligence for the IoT requires to stream huge amounts of data from edge sensors towards the cloud\, where deep learning models interpret the data. EdgeAI moves deep learning models from the cloud onto the Edge platforms themselves offering huge gains in terms of connectivity requirements\, energy\, cost\, privac y and end-to-end latency. tinyML or Extreme Edge AI moves the deep learnin g tasks even further right onto the microcontrollers connected to the sens ors.This class provides an in-depth introduction to TinyML\, the field of deploying machine learning (ML) models on resource-constrained edge device s like microcontrollers and sensors. Participants will learn the full pipe line of developing efficient ML models optimized for low-power\, low-memor y environments. The class covers the essential stages: model preparation\, where participants will explore data preprocessing\, feature selection\, and designing lightweight models suitable for edge hardware\; model evalua tion\, focusing on performance metrics\, accuracy trade-offs\, and model c ompression techniques to ensure feasibility on small devices as well as ev aluation in the context of embedded applications\; and finally\, code gene ration\, where participants will learn how to convert models into deployab le code using tools like TensorFlow Lite or TVM\, generating firmware for real-world applications.   Agenda & ContentFor a detailed timetable and additional information\, please see Agenda & Content in the left menu.Tar get audience\, eligibility & pricesEmbedded Software Developers that want to learn about how to integrate deep learning into embedded applicationsTh is course is open and free of charge for all participants from academia\, industry\, and public administration from EU and/or EuroHPC JU member coun tries.Entry level & prerequisitesIntermediate – Some python experience a nd experience in embedded software development is required. Course format This course will be delivered as a HYBRID Course via ZOOM ONLINE / LIVE @ AI Factory Austria AI:AT\, Karl-Farkas-Gasse 22\, 1030 Wien LecturerDanie l Mueller-Gritschneder (TU Wien)                       Daniel Mueller-Gritschneder is a full professor at the Institute of Comp uter Engineering at TU Wien Informatics\, Austria\, a position he has held since 2024. Prior to this\, he served as a research group leader and acti ng professor for Real-Time Systems at the Technical University of Munich ( TUM)\, Germany. He received his Dipl.-Ing.\, Dr.-Ing.\, and habilitation d egrees from TUM in 2003\, 2009\, and 2019\, respectively. Throughout his c areer\, he has contributed to numerous collaborative research projects in TinyML and Edge AI\, working closely with industry partners including Infi neon\, Bosch\, and BMW. His research focuses on the optimization of deep l earning models and deployment toolchains for embedded platforms\, spanning tiny microcontrollers to high-performance central compute units. He is a regular program committee member for leading EDA conferences such as DAC\, ICCAD\, DATE\, SAMOS\, and CODES+ISSS\, and is active in the RISC-V commu nity\, including contributions to the RISC-V Summit Europe. Recent works h e co-authored were recognized with the best paper award at the SAIAD Works hop of CVPR2025\, the CODAI workshop 2023\, as well as the Best Student Pa per Award at the WACV 2024. Philipp van Kempen (TU München)   Philipp van Kempen works in the Electronic System Level (ESL) group at the TUM Ch air for Electronic Design Automation (EDA)\, developing ML and software co mpiler solutions to tackle deployment challenges for Extreme Edge AI (Tiny ML) applications. His research focuses on automatic retargeting of embedde d workloads to customized RISC-V platforms. He also has a strong backgroun d in virtual prototyping and a passion for open-source ecosystems. Langua geEnglishDate\, time\, and location13.05.2026\, 09:30 – 12:30 (CET)\, vi a ZOOM ONLINE / LIVE @ AI Factory Austria AI:AT\, Karl-Farkas-Gasse 22\, 1 030 Wien RegistrationRegistration is required\, the registration form can be found on top of this page in the menu on the left.Please register with your official institutional email address to prove your affiliation.You w ill get an automatic confirmation by email (subject starting with "[Indico ] Registration")\, please check your Spam/Junk folders.Following your succ essful registration\, you will receive further information a few days befo re the course.Please do not hesitate to contact us at training@ai-at.eu if you have any questions.WaitinglistAfter the number of registrations has r eached its maximum or the registration form has been closed\, you may want to send us an email (training@ai-at.eu) stating that you are interested t o be put on the waiting list (vacancies may occur due to cancelations\, et c.).Modification\, withdrawal & no-show policyYour registration is binding . Please only register for the course if you are really going to attend.Yo u can update your registration data or withdraw your registration anytime before the registration form has been closed via the link "Manage my regis tration" which you can find at the bottom of your automatic email confirma tion (subject starting with "[Indico] Registration").Alternatively\, or af ter the registration form has been closed\, please inform us about your ca ncelation or any change in your registration data (especially your mobile- phone number) via email (training@ai-at.eu).No-show policy: If you do not cancel and do not show up at the course you will be blacklisted and exclud ed from future training events.OrganizersThis course is organized by AI Fa ctory Austria AI:AT.AcknowledgementsAI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU ) under grant agreement No 101253078. The JU receives support from the Hor izon Europe Programm of the European Union and Austria (BMIMI / FFG). \n\ nhttps://events.asc.ac.at/event/287/ LOCATION:Zoom (ONLINE) URL:https://events.asc.ac.at/event/287/ END:VEVENT END:VCALENDAR