3 citations · 7 across the 7 of their papers we have counts for
7 papers
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…