Papers
3
Total Citations
19
H-Index
3
About
Hamoud Younes is a leading researcher at the intersection of embedded machine learning and tactile sensing, specializing in the deployment of efficient neural networks on resource-constrained hardware. His work centers on enabling artificial intelligence for edge and Internet-of-Things (IoT) applications, with a particular focus on electronic skin systems. Younes made a significant contribution with his 2022 study on memory-efficient binary convolutional neural networks for microcontrollers, which demonstrated how binarization—a key optimization technique—can drastically reduce memory footprints while maintaining accuracy for industrial field applications. This work, his most cited with 10 citations, addresses a critical bottleneck in deploying deep learning on low-power devices. He further advanced the field by developing a tiny CNN for embedded electronic skin systems (5 citations) and pioneering near-sensor computation architectures for tactile data decoding (4 citations), enabling real-time texture classification and pattern recognition directly on distributed sensor arrays. Younes’ research bridges the gap between advanced machine learning algorithms and practical, low-power embedded systems, making him a notable figure in the push toward intelligent, autonomous edge devices for industrial and wearable applications.
Research Focus
Key Achievements
Top Papers
- 1Memory Efficient Binary Convolutional Neural Networks on Microcontrollers10 citations · 2022
- 2A Tiny CNN for Embedded Electronic Skin Systems5 citations · 2022
- 3