7 citations · 15 across the 7 of their papers we have counts for
10 papers
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
Emil Njor, Colby Banbury, Xenofon Fafoutis
Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-p…
EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools
Mohammad Amin Hasanpour, Mikkel Kirkegaard, Xenofon Fafoutis
The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
HRL-TSCH: A Hierarchical Reinforcement Learning-based TSCH Scheduler for IIoT
F. Fernando Jurado-Lasso, Charalampos Orfanidis, J. F. Jurado +1
The Industrial Internet of Things (IIoT) demands adaptable Networked Embedded Systems (NES) for optimal performance. Combined with recent advances in Artificial Intelligence (AI),…
Data Aware Neural Architecture Search
Emil Njor, Jan Madsen, Xenofon Fafoutis
Neural Architecture Search (NAS) is a popular tool for automatically generating Neural Network (NN) architectures. In early NAS works, these tools typically optimized NN architectu…
Active Connectivity Fundamentals for TSCH Networks of Mobile Robots
Charalampos Orfanidis, Paul Pop, Xenofon Fafoutis
Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard which have been proven to be one of the most reliable options when it comes to…