3 citations · 4 across the 2 of their papers we have counts for
12 papers
Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module
Simone Lugani, Edoardo Ragusa, Rodolfo Zunino +1
The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem. On one hand,…
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras
Edoardo Ragusa, Giovanni Paolo Canuti, Simone Lugani +2
The paper proposes hardware‑aware neural architecture search and a fine‑tuning pipeline to integrate depth data into compact RGB‑D networks for affordance segmentation on embedded…
BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2
This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor proce…
Searching Neural Architectures for Sensor Nodes on IoT Gateways
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT…
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural arch…
On-Device Neural Architecture Search
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2
This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best…