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Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles [article]

Miguel de Prado, Manuele Rusci, Romain Donze, Alessandro Capotondi, Serge Monnerat, Luca Benini and, Nuria Pazos
2021 arXiv   pre-print
To address them, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop.  ...  Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning.  ...  ROBUST NAVIGATION WITH TINYML We aim to take a tinyML approach and replace the initial CVA solution by a tinyCNN.  ... 
arXiv:2007.00302v2 fatcat:kxylimhhqrcutehe2dfqpa2nhu

Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles

Miguel de de Prado, Manuele Rusci, Alessandro Capotondi, Romain Donze, Luca Benini, Nuria Pazos
2021 Sensors  
To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop.  ...  However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities.  ...  Figure 2 . 2 Challenges and methodology for robust and efficient deployment with tinyML for autonomous low-power driving vehicles. Figure 3 . 3 Closed-loop learning pipeline.  ... 
doi:10.3390/s21041339 pmid:33668645 pmcid:PMC7918899 fatcat:krfqzoi5pnbnfnyn3gndzj7j3q

Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles

Miguel de Prado, Manuele Rusci, Romain Donze, Alessandro Capotondi, Serge Monnerat, Luca Benini, Nuria Pazos
2021 2021 IEEE International Symposium on Circuits and Systems (ISCAS)  
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles. Sensors  ...  Figure 2 . 2 Challenges and methodology for robust and efficient deployment with tinyML for autonomous low-power driving vehicles. Figure 3 . 3 Closed-loop learning pipeline.  ...  Table 2 shows the different network configurations -networks and datasets have been opensourced from https://github.com/praesc/Robust-navigation-with-TinyML.  ... 
doi:10.1109/iscas51556.2021.9401154 fatcat:cqieoaxwm5cwxhgbellngethze

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [article]

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
2021 arXiv   pre-print
In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models.  ...  By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence  ...  Edge learning over a vehicle-to-everything network is critical to enable autonomous driving with delay-sensitive applications [145] .  ... 
arXiv:2111.12444v1 fatcat:crrbtfylvjeihogumggdnxcbpq

D1.1 - State of the Art Analysis

Danilo Ardagna
2021 Zenodo  
The deliverable starts with an overview of AI applications and edge computing market trends.  ...  ., devices with intelligence and data processing capabilities), providing resource efficiency, performance, data privacy, and security guarantees.  ...  as voice and image recognition, machine translation, control of assisted driving and autonomous vehicle navigation.  ... 
doi:10.5281/zenodo.6372377 fatcat:f6ldfuwivbcltew4smiiwphfty