6 papers
Fully-sensorized smart-eyewear platform for on-device Machine Learning
Andrea Giudici, Christian Veronesi, Pietro Bartoli +5
This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency. Unlike cloud-dependent solutions, ARG…
Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices
Pietro Bartoli, Christian Veronesi, Tommaso Bondini +2
Gesture recognition is a cornerstone of Human-Computer Interaction (HCI) for smart eyewear, enabling natural and device-free control in augmented reality environments. Traditional…
Hardware-Aware Neural Feature Extraction for Resource-Constrained Devices
Francesco Tosini, Simone Pedroni, Christian Veronesi +5
Visual SLAM is a core component of spatial computing systems, yet deploying learned local feature extractors on microcontroller-class hardware remains challenging due to memory, ba…
End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Pietro Bartoli, Tommaso Bondini, Christian Veronesi +3
Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment o…
Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI
Pietro Bartoli, Christian Veronesi, Andrea Giudici +3
The rise of IoT has increased the need for on-edge machine learning, with TinyML emerging as a promising solution for resource-constrained devices such as MCU. However, evaluating…
On-Sensor Convolutional Neural Networks with Early-Exits
Hazem Hesham Yousef Shalby, Arianna De Vecchi, Alice Scandelli +4
Tiny Machine Learning (TinyML) is a novel research field aiming at integrating Machine Learning (ML) within embedded devices with limited memory, computation, and energy. Recently,…