TinyML4D: Scaling Embedded Machine Learning Education in the Developing World


  • Brian Plancher Barnard College, Columbia University
  • Sebastian Buttrich IT University of Copenhagen
  • Jeremy Ellis School District 75 Mission
  • Neena Goveas BITS Pilani K K Birla - Goa Campus
  • Laila Kazimierski Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) Centro Atomico Bariloche (CAB) Comision Nacional de Energia Atomica (CNEA)
  • Jesus Lopez Sotelo Universidad Autónoma de Occidente
  • Milan Lukic University of Novi Sad
  • Diego Mendez Pontificia Universidad Javeriana
  • Rosdiadee Nordin Sunway University
  • Andres Oliva Trevisan Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) Centro Atomico Bariloche (CAB)
  • Massimo Pavan Politecnico di Milano
  • Manuel Roveri Politecnico di Milano
  • Marcus Rüb Technische Universität München
  • Jackline Tum Dedan Kimathi University of Technology
  • Marian Verhelst KU Leuven
  • Salah Abdeljabar King Abdullah University of Science and Technology (KAUST)
  • Segun Adebayo Bowen University
  • Thomas Amberg University of Applied Sciences Northwestern Switzerland FHNW
  • Halleluyah Aworinde Bowen University
  • José Bagur Universidad del Valle de Guatemala
  • Gregg Barrett Cirrus
  • Nabil Benamar Moulay Ismail University
  • Bharat Chaudhari MIT World Peace University
  • Ronald Criollo Escuela Superior Politécnica del Litoral
  • David Cuartielles Malmö University
  • Jose Alberto Ferreira Filho Universidade Federal de Itajubá (UNIFEI)
  • Solomon Gizaw Addis Ababa University
  • Evgeni Gousev Qualcomm
  • Alessandro Grande Edge Impulse
  • Shawn Hymel Edge Impulse
  • Peter Ing TFG (The Foschini Group)
  • Prashant Manandhar Center for Information and Communication Technology for Development
  • Pietro Manzoni Universitat Politècnica de València
  • Boris Murmann University of Hawaii
  • Eric Pan Seeed Studio
  • Rytis Paskauskas The Abdus Salam International Centre for Theoretical Physics (ICTP)
  • Ermanno Pietrosemoli The Abdus Salam International Centre for Theoretical Physics (ICTP)
  • Tales Pimenta Universidade Federal de Itajubá (UNIFEI)
  • Marcelo Rovai Universidade Federal de Itajubá (UNIFEI)
  • Marco Zennaro The Abdus Salam International Centre for Theoretical Physics (ICTP)
  • Vijay Janapa Reddi Harvard University




Increasing Diversity in AI Education and Research


Embedded machine learning (ML) on low-power devices, also known as "TinyML," enables intelligent applications on accessible hardware and fosters collaboration across disciplines to solve real-world problems. Its interdisciplinary and practical nature makes embedded ML education appealing, but barriers remain that limit its accessibility, especially in developing countries. Challenges include limited open-source software, courseware, models, and datasets that can be used with globally accessible heterogeneous hardware. Our vision is that with concerted effort and partnerships between industry and academia, we can overcome such challenges and enable embedded ML education to empower developers and researchers worldwide to build locally relevant AI solutions on low-cost hardware, increasing diversity and sustainability in the field. Towards this aim, we document efforts made by the TinyML4D community to scale embedded ML education globally through open-source curricula and introductory workshops co-created by international educators. We conclude with calls to action to further develop modular and inclusive resources and transform embedded ML into a truly global gateway to embedded AI skills development.






Increasing Diversity in AI Education and Research