52 citations · 267 across the 37 of their papers we have counts for
15 papers · 1 filter
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
Francesco Daghero, Alessio Burrello, Chen Xie +6
Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computati…
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
Khaled Sidahmed Sidahmed Alamin, Yukai Chen, Enrico Macii +2
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject…
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs
Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino
Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classi…
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes
Matteo Risso, Alessio Burrello, Luca Benini +3
Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precis…
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks
Matteo Risso, Alessio Burrello, Luca Benini +3
Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When t…
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…
C-NMT: A Collaborative Inference Framework for Neural Machine Translation
Yukai Chen, Roberta Chiaro, Enrico Macii +2
Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for ot…
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,…
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari +5
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization…
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
Alessio Burrello, Francesco Bianco Morghet, Moritz Scherer +5
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographi…
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices
Alessio Burrello, Daniele Jahier Pagliari, Matteo Risso +4
Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements o…
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
Alessio Burrello, Alberto Dequino, Daniele Jahier Pagliari +5
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optim…
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring
Alessio Burrello, Daniele Jahier Pagliari, Pierangelo Maria Rapa +6
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs)…