3 citations · 7 across the 7 of their papers we have counts for
8 papers
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…
An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Sara Rimoldi, Arianna De Vecchi, Hazem Hesham Yousef Shalby +1
Wearable devices running Human Activity Recognition(HAR) on Inertial Measurement Units~(IMUs) waste energy by performing continuous classification for each window, even during long…
DQT: Dynamic Quantization Training via Dequantization-Free Nested Integer Arithmetic
Hazem Hesham Yousef Shalby, Fabrizio Pittorino, Francesca Palermo +2
The deployment of deep neural networks on resource-constrained devices relies on quantization. While static, uniform quantization applies a fixed bit-width to all inputs, it fails…
InfoQ: Mixed-Precision Quantization via Global Information Flow
Mehmet Emre Akbulut, Hazem Hesham Yousef Shalby, Fabrizio Pittorino +1
Mixed-precision quantization (MPQ) is crucial for deploying deep neural networks on resource-constrained devices, but finding the optimal bit-width for each layer represents a comp…
On-Sensor Convolutional Neural Networks with Early-Exits
Hazem Hesham Yousef Shalby, Arianna De Vecchi, Alice Scandelli +4
Tiny Machine Learning (TinyML) is a novel research field aiming at integrating Machine Learning (ML) within embedded devices with limited memory, computation, and energy. Recently,…
Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
Hazem Hesham Yousef Shalby, Manuel Roveri
Human activity recognition (HAR) is a research field that employs Machine Learning (ML) techniques to identify user activities. Recent studies have prioritized the development of H…
EmbBERT: Attention Under 2 MB Memory
Riccardo Bravin, Massimo Pavan, Hazem Hesham Yousef Shalby +2
Transformer architectures based on the attention mechanism have revolutionized natural language processing (NLP), driving major breakthroughs across virtually every NLP task. Howev…
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…