7 citations · 32 across the 21 of their papers we have counts for
8 papers · 1 filter
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals
Luca Colombo, Fabrizio Pittorino, Manuel Roveri
Binary Neural Networks (BNNs) significantly reduce computational complexity and memory usage in machine and deep learning by representing weights and activations with just one bit.…
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment
Luca Colombo, Alessandro Falcetta, Manuel Roveri
Training machine and deep learning models directly on extremely resource-constrained devices is the next challenge in the field of tiny machine learning. The related literature in…
StreamTinyNet: video streaming analysis with spatial-temporal TinyML
Hazem Hesham Yousef Shalby, Massimo Pavan, Manuel Roveri
Tiny Machine Learning (TinyML) is a branch of Machine Learning (ML) that constitutes a bridge between the ML world and the embedded system ecosystem (i.e., Internet of Things devic…
NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks
Alberto Pirillo, Luca Colombo, Manuel Roveri
Quantization is a pivotal technique for managing the growing computational and memory demands of Deep Neural Networks (DNNs). By reducing the number of bits used to represent weigh…
TinySV: Speaker Verification in TinyML with On-device Learning
Massimo Pavan, Gioele Mombelli, Francesco Sinacori +1
TinyML is a novel area of machine learning that gained huge momentum in the last few years thanks to the ability to execute machine learning algorithms on tiny devices (such as Int…
A Lightweight Neural Architecture Search Model for Medical Image Classification
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and…
FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness
Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
Neural Architecture Search (NAS) paves the way for the automatic definition of Neural Network (NN) architectures, attracting increasing research attention and offering solutions in…
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks
Matteo Gambella, Jary Pomponi, Simone Scardapane +1
Early Exit Neural Networks (EENNs) endow astandard Deep Neural Network (DNN) with Early Exit Classifiers (EECs), to provide predictions at intermediate points of the processing whe…