Cross-layer optimization for joint visual-inertial localization and object detection on resource-constrained devices
Examiner Karl H. Johansson
Time: Wed 2021-06-23 10.00 - 10.30
Respondent: Elisa Baldassari , Reglerteknik - DCS
Opponent: Gianlorenzo Moser
Supervisor: Roberto Morabito (Ericsson AB), José Araújo (Ericsson AB), Amr Alanwar Abdelhafez (KTH)
The expectations in performing high-performance cyber-physical applications in resource-constrained devices are continuously increasing. The available hardware is still a main limitation in this context, both in terms of computation capability and energy limits. On the other hand, one must ensure the robust and accurate execution of the applications deployed, since their failure may entail risks for humans and the surrounding environment. The limits and risks are enhanced when multiple applications are executed on the same device. The focus of this thesis is to provide a trade-off between the required performance and power consumption. The focus is on two fundamental applications in the mobile autonomous vehicles scenario: localization and object detection. The multi-objective optimization is performed in a cross-layer manner, exploring both applications and platform configurable parameters with Design Space Exploration (DSE). The focus is on localization and detection accuracy, detection latency and power consumption. Predictive models are designed to estimate the metrics of interest and ensure robust execution, excluding potential faulty configurations from the design space. The research is approached empirically, performing tests on the Nvidia Jetson AGX and NX platforms. Results show that optimal configurations for a single application are in general sub-optimal or faulty for the concurrent execution case, while the opposite is sometimes applicable.