Abstract
Deep learning is moving more and more from the cloud towards the edge. Therefore, embedded devices are needed that are reasonably cheap, energy-efficient and fast enough. In this paper we evaluate the performance and energy consumption of popular, off-the-shelf commercial devices for deep learning inferencing. We compare the Intel Neural Compute Stick 2, the Google Coral Edge TPU and the Nvidia Jetson Nano with the Raspberry Pi 4 for their suitability as a central controller in an autonomous vehicle for the formula student driverless.
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Puchtler, P., & Peinl, R. (2020). Evaluation of deep learning accelerators for object detection at the edge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12325 LNAI, pp. 320–326). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58285-2_29
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