Embedded Artificial Intelligence Bridging the Gap Between Hardware and Deep Learning

The IEEE CAS Seasonal School on Technologies for Artificial Intelligence tackles the critical skill gap between embedded technology and deep learning. Supported by European projects FVLLMONTI, HERMES, and RadioSpin, this event fosters a transdisciplinary community focused on embedded artificial intelligence. Modern AI and deep learning often require extensive computing resources, impacting security and privacy. Embedded AI offers a solution by running machine learning models on edge devices, necessitating optimized softwarehardware integration and energy-efficient neural network hardware. This school equips participants with the skills to innovate in circuit design and execute data-intensive applications on limited-resource devices. The curriculum covers neural network basics, hardware enhancement, electrical characterization, and neuromorphic device design. Key topics include 6G transceiver optimization, transformer architectures for machine translation, and intelligent sensors for practical applications like RF fingerprint recognition and breast cancer detection. Based on the IEEE CAS Seasonal School on Technologies for Artificial Intelligence, this book tackles the critical skill gap between embedded technology and deep learning. It equips readers with the skills to innovate in circuit design and execute data-intensive applications on limited-resource devices.

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