GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Improvement of on-board object identification performance of autonomous nano-drones through the use of deep learning techniques and neural architecture search.
keywords ARTIFICIAL INTELLIGENCE, CONVOLUTIONAL NEURAL NETWORKS, DEEP LEARNING, DEEP NEURAL NETWORKS, DRONES, EMBEDDED SYSTEMS, ENERGY EFFICIENCY, FIRMWARE DEVELOPMENT, LOW POWER, MICROCONTROLLERS, ROBOTICS, SOFTWARE, UAV
Reference persons DANIELE JAHIER PAGLIARI
External reference persons Dr. Alessio Burrello (Politecnico di Torino), Dr. Lorenzo Lamberti (University of Bologna), Dr. Daniele Palossi (SUPSI-IDSIA, Lugano)
Thesis type EMBEDDED SOFTWARE DEVELOPMENT, EXPERIMENTAL, IN-FIELD TESTING, SOFTWARE DEVELOPMENT
Description The candidate will focus on enhancing the performance of autonomous nano-drones in environmental exploration and object identification tasks, by using deep learning techniques and neural architecture search (NAS) algorithms. The scope of the study revolves around the creation of synthetic data from a simulator to improve the end-to-end performance of the drone. Specifically, during this thesis, the candidate will try to reach the following milestones:
1. Propose a new NAS algorithm that is specifically designed to enhance the object recognition performance of autonomous nano-drones.
2. Include data generated by a simulator in the NAS search loop, to achieve an improvement of the end-to-end environment exploration and object identification task performed by the nano-drone.
3. Test the final neural network found by the NAS in-field on real-world data.
The study aims to achieve better end-to-end performance by, co-optimizing the object recognition performance on individual frames, by reducing false positives and negatives, and the processing time required by the neural network, which is crucial for the efficient operation of autonomous nano-drones.
Required skills Required skills include C and Python programming. Further, a basic knowledge of computer architectures and embedded systems is necessary. Lastly, the candidate should also be familiar with machine/deep learning algorithms and models.
Notes Thesis in collaboration with Prof. Luca Benini and Dr. Lorenzo Lamberti, from the University of Bologna, and Dr. Daniele Palossi from SUPSI – IDSIA, Lugano.
Deadline 06/06/2024 PROPONI LA TUA CANDIDATURA