Francesco Bellotti

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About

Francesco Bellotti is a leading researcher in embedded artificial intelligence, with a focus on deploying advanced machine learning models on resource-constrained devices. His work centers on optimizing neural networks for microcontrollers and edge systems, addressing critical challenges in memory efficiency and real-time processing. Bellotti’s major contributions include pioneering memory-efficient binary convolutional neural networks for microcontrollers, a breakthrough that enables high-performance AI on low-power industrial platforms. His research on affordance detection pipelines for semi-autonomous systems, integrating human-in-the-loop scenarios, advances practical robotics and human-computer interaction. Additionally, his development of tiny CNNs for embedded electronic skin systems pushes the boundaries of tactile sensing in wearable and robotic applications. With over 20 citations across his most-cited papers, Bellotti’s impact is evident in the growing adoption of his methods for edge AI. His notable achievements include demonstrating that binarization techniques can achieve significant results in field applications, bridging the gap between theoretical optimization and real-world deployment. Bellotti’s work is essential for students and researchers exploring efficient AI for IoT, robotics, and embedded systems.