3 papers
cs.LG2025
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
Idris Zakariyya, Ferheen Ayaz, Mounia Kharbouche-Harrari +4
Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge dev…
cs.LG2025
Biases in Edge Language Models: Detection, Analysis, and Mitigation
Vinamra Sharma, Danilo Pietro Pau, José Cano
The integration of large language models (LLMs) on low-power edge devices such as Raspberry Pi, known as edge language models (ELMs), has introduced opportunities for more personal…
cs.LG2025
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers
Martin Joel Mouk Elele, Danilo Pau, Shixin Zhuang +1
The deployment of neural networks on resource-constrained micro-controllers has gained momentum, driving many advancements in Tiny Neural Networks. This paper introduces a tiny fee…