7 citations · 32 across the 21 of their papers we have counts for
4 papers · 1 filter
Memory-Efficient Integer-Only Training of Deep Neural Networks
Hazem Hesham Yousef Shalby, Fabrizio Pittorino, Manuel Roveri
Training neural networks in reduced precision can yield significant efficiency gains, yet all existing methods that scale to standard computer vision benchmarks retain floating-poi…
What changes after deployment? A survey on On-device Learning in TinyML
Massimo Pavan, Luca Pezzarossa, Fabrizio Pittorino +2
Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static models. On-device learning (OD…
HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search
Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and efficiency. However, as AI transit…
SQUAD: Scalable Quorum Adaptive Decisions via ensemble of early exit neural networks
Matteo Gambella, Fabrizio Pittorino, Giuliano Casale +1
Early-exit neural networks have become popular for reducing inference latency by allowing intermediate predictions when sufficient confidence is achieved. However, standard approac…