48 citations · 154 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Robust and Energy-efficient PPG-based Heart-Rate Monitoring
Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari +4
A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring…
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…
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…
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)…