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Intro
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Motivation/Vision
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State-of-the-Art and Challenges
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How to enable the extreme edge computing?
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Full stack application: Autonomous navigation
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On-board brain
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PULP-Dronet: Shrinking and optimization
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PULP-Dronet: Results
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PULP-Dronet: In-field evaluation
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PULP-Dronet evolution
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PULP-Dronet v2: Results
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PULP-Dronet v3: Tiny-PULP-Dronets
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Full stack application: Human robot interaction
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PULP-Frontnet: In-field evaluation
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How to improve the generalization capability?
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Background randomization: Pipeline
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Background randomization: Testing setup
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Background randomization: Results
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Background randomization: In-field evaluation
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How to improve the regression performance?
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Vision-state fusion: In-field evaluation
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Conclusion
Description:
Explore the cutting-edge research on autonomous nano-UAVs in this conference talk from tinyML EMEA 2022. Delve into the challenges and solutions for enabling extreme edge computing on miniature flying robots with stringent size and power constraints. Learn about innovative approaches using machine learning and deep neural networks to achieve onboard intelligence, including vision-based algorithms, quantization techniques, and data augmentation pipelines. Discover key projects like PULP-Dronet and PULP-Frontnet, addressing crucial questions about optimizing CNNs for autonomous navigation, improving generalization for human-robot interaction, and combining cyber-physical system state with vision-based CNNs. Gain insights into shrinking operation counts and memory footprints, enhancing tiny CNN performance, and integrating vision with state information. Examine thorough in-field evaluations and closed-loop robotic demonstrations that showcase the practical applications of these groundbreaking methodologies in nanorobotics. Read more

Autonomous Nano-UAVs: An Extreme Edge Computing Case

tinyML
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