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An automotive case study on the limits of approximation for object detection., , , , , , , , , and 2 other author(s). J. Syst. Archit., (2023)Generating and Exploiting Deep Learning Variants to Increase Heterogeneous Resource Utilization in the NVIDIA Xavier., , , , , and . ECRTS, volume 133 of LIPIcs, page 23:1-23:23. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)Assessing the Adherence of an Industrial Autonomous Driving Framework to ISO 26262 Software Guidelines., , , , and . DAC, page 9. ACM, (2019)Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations., , , , , , , and . Remote. Sens., 13 (24): 5003 (2021)Empirical Evidence for MPSoCs in Critical Systems: The Case of NXP's T2080 Cache Coherence., , , , and . DATE, page 1162-1165. IEEE, (2021)Optimal task allocation for maximizing reliability in distributed real-time systems., , , and . ICIS, page 513-519. IEEE Computer Society, (2013)IntPred: flexible, fast, and accurate object detection for autonomous driving systems., , , , and . SAC, page 564-571. ACM, (2020)Vector Extensions in COTS Processors to Increase Guaranteed Performance in Real-Time Systems., , , , , , and . ACM Trans. Embed. Comput. Syst., 22 (2): 21:1-21:26 (March 2023)At-Scale Evaluation of Weight Clustering to Enable Energy-Efficient Object Detection., , and . CoRR, (2023)Enabling Unit Testing of Already-Integrated AI Software Systems: The Case of Apollo for Autonomous Driving., , , and . DSD, page 426-433. IEEE, (2021)