ReceptroniX
Project Overview
University
Year
About
Many AI systems rely on cloud computing, creating delays, consuming energy and exposing sensitive data during transmission. ReceptroniX develops specialised hardware that runs advanced AI directly on devices with limited processing power, memory and battery life. Its core “receptron” replaces the simple linear calculations used in conventional neural networks with flexible, input-dependent functions, allowing each unit to recognise more complex patterns. This could create smaller, faster models that use less data and energy, enabling responsive and private AI in sensors, wearables, robots and other embedded systems.
Team
Riccardo Emanuele Beccalli
University Scholar working on unconventional computing architectures for next-generation edge AI solutions. Deep tech enthusiast passionate about bridging frontier research and real-world applications, with a strong focus on translating emerging technologies into scalable innovation. Interested in the intersection of advanced computing, artificial intelligence, and entrepreneurship, with the goal of contributing to impactful technologies that can shape the future of intelligent systems.
Stefano Radice
Stefano Radice is a third-year PhD student in Physics at the University of Milan, where he also earned his Master's degree in Condensed Matter Physics. His research focuses on nanostructured materials and neuromorphic computing.
Ludovico Casaccia
Ludovico Casaccia is a physicist and tech entrepreneur bridging the gap between deep-tech research and product development. Holding an MSc in Physics from the Università degli Studi di Milano, his expertise centers on neuromorphic computing and Edge AI architectures. With an award-winning background in international physics competition and driven by a passion for applied science, he translates complex theoretical frameworks into tangible industrial products.