AIEng4D
AI Engineering for Global Impact
Learning
Ecosystem
Explore our curriculum: open-access textbooks, hardware kits, and hands-on laboratories for scaling intelligence.
Learning Resources
ML Systems
Foundations of scalable AI. Learn how machine learning systems are built for production at scale.
TinyTorch
Deep learning from scratch. Build neural networks without frameworks to understand every detail.
Hardware Kits
Connecting code to physics. Deploy ML models on embedded devices and microcontrollers.
Recommended Reading
These recommended readings provide a strong introduction to TinyML and embedded machine learning systems, from foundational technical challenges and benchmarking to real-world educational applications. Together, they highlight how resource-efficient AI can expand access to practical, locally relevant technology and learning opportunities worldwide.
Reddi, V.J., Plancher, B., Kennedy, S., Moroney, L., Warden, P., Agarwal, A., Banbury, C., Banzi, M., Bennett, M., Brown, B. and Chitlangia, S., 2021. Widening access to applied machine learning with tinyml. arXiv preprint arXiv:2106.04008.
Read PaperPlancher, B., Buttrich, S., Ellis, J., Goveas, N., Kazimierski, L., Sotelo, J.L., Lukic, M., Mendez, D., Nordin, R., Trevisan, A.O. and Pavan, M., 2024, May. TinyML4D: scaling embedded machine learning education in the developing world. In Proceedings of the AAAI symposium series (Vol. 3, No. 1, pp. 508-515).
Read PaperRavindran, Sandeep. "What's tinyML? The Global South's Alternative to Power-Hungry, Pricey AI." Science, 2025, www.science.org/content/article/what-s-tinyml-global-south-s-alternative-power-hungry-pricey-ai.
Read PaperBanbury, C.R., Reddi, V.J., Lam, M., Fu, W., Fazel, A., Holleman, J., Huang, X., Hurtado, R., Kanter, D., Lokhmotov, A. and Patterson, D., 2020. Benchmarking tinyml systems: Challenges and direction. arXiv preprint arXiv:2003.04821.
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