Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones-Reference-Cited by-同舟云学术

Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones

Published:2022-06-13 Issue: Volume: Page:
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Container-title:2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
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Author:

Lamberti Lorenzo¹,Niculescu Vlad²,Barcis Michal³,Bellone Lorenzo³,Natalizio Enrico³,Benini Luca¹,Palossi Daniele²

Affiliation:

1. University of Bologna,Department of Electrical, Electronic and Information Engineering,Italy

2. ETH Zürich,Integrated Systems Laboratory,Switzerland

3. Technology Innovation Institute,Autonomous Robotics Research Center,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9869844/9869845/09869931.pdf?arnumber=9869931

Reference13 articles.

1. Automated Tuning of End-to-end Neural Flight Controllers for Autonomous Nano-drones

2. Deep Residual Learning for Image Recognition

3. Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks;hoefler;Journal of Machine Learning Research,2021

4. RocketLogger

5. Mitigating latency problems in vision-based autonomous UAVs

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