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dc.contributor.author
Niculescu, Vlad
dc.contributor.author
Lamberti, Lorenzo
dc.contributor.author
Conti, Francesco
dc.contributor.author
Benini, Luca
dc.contributor.author
Palossi, Daniele
dc.date.accessioned
2022-01-26T11:24:17Z
dc.date.available
2021-12-31T03:42:52Z
dc.date.available
2022-01-26T11:24:17Z
dc.date.issued
2021-12
dc.identifier.issn
2156-3357
dc.identifier.other
10.1109/JETCAS.2021.3126259
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/522503
dc.description.abstract
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10cm robotic platforms are envisioned as next-generation ubiquitous smart-sensors and unobtrusive robotic-helpers. However, the limited computational/memory resources available aboard nano-UAVs introduce the challenge of minimizing and optimizing vision-based CNNs - which to date require error-prone, labor-intensive iterative development flows. This work explores methodologies and software tools to streamline and automate all the deployment of vision-based CNN navigation on a ULP multicore system-on-chip acting as a mission computer on a Crazyflie 2.1 nano-UAV. We focus on the deployment of PULP-Dronet (Palossi et al., 2019), a state-of-the-art CNN for autonomous navigation of nano-UAVs, from the initial training to the final closed-loop evaluation. Compared to the original hand-crafted CNN, our results show a 2x reduction of memory footprint and a speedup of 1.6x in inference time while guaranteeing the same prediction accuracy and significantly improving the behavior in the field, achieving: i) obstacle avoidance with a peak braking-speed of 1.65 m/s and improving the speed/braking-space ratio of the baseline, free flight in a familiar environment up to 1.96 m/s (0.5 m/s for the baseline), and iii) lane following on a path featuring a 90deg turn - all while using for computation less than 1.6% of the drone's power budget. To foster new applications and future research, we open-source all the software design in a ready-to-run project compatible with the Crazyflie 2.1.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Improving Autonomous Nano-Drones Performance via Automated End-to-End Optimization and Deployment of DNNs
en_US
dc.type
Journal Article
dc.date.published
2021-11-13
ethz.journal.title
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
ethz.journal.volume
11
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
IEEE j. emerg. sel. top. circuits syst.
ethz.pages.start
548
en_US
ethz.pages.end
562
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
ethz.date.deposited
2021-12-31T03:43:48Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-01-26T11:24:35Z
ethz.rosetta.lastUpdated
2022-01-26T11:24:35Z
ethz.rosetta.versionExported
true
ethz.COinS
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