Edge Devices Inference Performance Comparison

Rafal Tobiasz; Grzegorz Wilczynski; Piotr Graszka; Nikodem Czechowski; Sebastian Luczak

doi:10.5626/JCSE.2023.17.2.51

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자료유형: 학술저널

저자정보: Rafal Tobiasz (Bulletprove Spółka Z Ograniczoną Odpowiedzialnością) Grzegorz Wilczynski (Bulletprove Spółka Z Ograniczoną Odpowiedzialnością) Piotr Graszka (Bulletprove Spółka Z Ograniczoną Odpowiedzialnością) Nikodem Czechowski (Bulletprove Spółka Z Ograniczoną Odpowiedzialnością) Sebastian Luczak (Bulletprove Spółka Z Ograniczoną Odpowiedzialnością)

저널정보: Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.17 No.2

발행연도: 2023.6

수록면: 51 - 59 (9page)

DOI: 10.5626/JCSE.2023.17.2.51

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In this study, we investigated the inference time of the MobileNet family, EfficientNet V1 and V2 family, VGG models, ResNet family, and InceptionV3 on four edge platforms: Specifically, NVIDIA Jetson Nano, Intel Neural Stick, Google Coral USB Dongle, and Google Coral PCIe. Our main contribution is a thorough analysis of the afore mentioned models in multiple settings, especially as a function of input size, the presence of the classification head, its size and the scale of the model. Since throughout the industry, these architectures are mainly used as feature extractors we majorly analyzed them as such. We show that Google platforms offer the fastest average inference time, especially for newer models like MobileNet or EfficientNet family, while Intel Neural Stick is the most universal accelerator allowing it to run most architectures. These results will provide guidance to engineers in the early stages of AI Edge systems development. The results are accessible at https://bulletprove.com/research/edge_inference_results.csv.

#Edge device #Deep learning #Computer vision

참고문헌 (44)

참고문헌 신청

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