activity
20222024
most citedHuman Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

52 citations · 106 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2024

Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks

Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello +3

The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mi…

cs.LG2024

Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices

Francesco Daghero, Alessio Burrello, Massimo Poncino +2

Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convo…

cs.CV2024

Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones

Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti +4

Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonethele…

cs.LG20241 cited

HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays

Matteo Risso, Chen Xie, Francesco Daghero +6

Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing e…

eess.SP20235 cited

Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation

Alessio Burrello, Matteo Risso, Noemi Tomasello +5

Modern smartwatches often include photoplethysmographic (PPG) sensors to measure heartbeats or blood pressure through complex algorithms that fuse PPG data with other signals. In t…

cs.LG2023

Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference

Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda +5

The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a di…