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.
Read PaperHardware Kit Companion Books
The books below pair with our recommended hardware kits and are maintained by the original authors.
TinyML Made Easy
Hands-on image classification, object detection, keyword spotting, and motion anomaly detection on the Nicla Vision board.
Marcelo Rovai · TinyML4D Co-Chair (UNIFEI)
TinyML Made Easy
Step-by-step TinyML projects for the Seeed XIAO ESP32S3 and Grove Vision AI, the hardware shipping via the Edge AI Foundation kit program.
Marcelo Rovai · TinyML4D Co-Chair (UNIFEI)
Edge AI Engineering
Edge AI beyond microcontrollers: computer vision, on-device LLMs, and AI-accelerator workflows on the Raspberry Pi platform.
Marcelo Rovai · TinyML4D Co-Chair (UNIFEI)
XIAO: Big Power, Small Board
From "turn on an LED" to TinyML. A full-journey Arduino and Seeed XIAO handbook; published with Seeed Studio, PDF professionally edited for print.
Marcelo Rovai · TinyML4D Co-Chair (UNIFEI)
UNO Q, GenAI at the Edge
Hands-on generative AI with the Arduino UNO Q: run small language models locally, add vision and tools, and build AI agents that can sense, reason, and interact with the physical world.
Marcelo Rovai · TinyML4D Co-Chair (UNIFEI)
