20 citations · 38 across the 4 of their papers we have counts for
4 papers
Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers
Francesco Daghero, Alessio Burrello, Chen Xie +5
Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks…
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors
Francesco Daghero, Chen Xie, Daniele Jahier Pagliari +6
Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning…
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
Chen Xie, Francesco Daghero, Yukai Chen +6
Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitori…
Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence
Francesco Daghero, Alessio Burrello, Daniele Jahier Pagliari +3
Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency,…