<p>➀ Researchers from TU Graz, Pro2Future, and the University of St. Gallen developed methods to deploy AI models on resource-constrained IoT devices with as little as 4KB memory, enabling localized processing without external compute resources; </p><p>➁ The team used techniques like model partitioning, Subspace-Configurable Networks (SCNs), quantization, and pruning to balance model size and accuracy, achieving up to 7.8x faster image processing on IoT devices; </p><p>➂ Applications include industrial automation (e.g., drone/robot localization), smart home remote controls, and anti-spoofing for keyless car entry systems, demonstrating broad scalability across embedded systems.</p>