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- W4383746592 abstract "Contemporary deep learning infrastructure heavily relies on Graphic Processing Units (GPU) as they have emerged as the most widely employed platforms for deep learning and artificial intelligence implementation. However, the utilization of GPU as the primary unit for processing data for Deep Learning and Artificial Intelligence applications encounters several impediments, including high costs and power consumption. Consequently, GPU may not be the optimal choice for many diverse cases where Deep Learning may be applied. This significantly restricts the scope and extent to which the algorithms and computations are utilized, thereby diminishing productivity. This scenario has prompted research endeavors that have focused on developing and designing edge-side artificial intelligence in recent times. In the aforementioned context, we conducted a study that evaluated three computing devices, namely the Seed Studio Sipeed Maixduino, an Android device, and a laptop equipped with an Nvidia Geforce GTX 1650, using the YOLOv2 architecture on the standard object detection workload algorithm and the COCO dataset. The evaluation metrics include accuracy, latency, and efficiency. This research provides a detailed report comparing the implementation of artificial intelligence on various edge devices." @default.
- W4383746592 created "2023-07-11" @default.
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- W4383746592 date "2023-05-26" @default.
- W4383746592 modified "2023-09-27" @default.
- W4383746592 title "Exploring Edge Artificial Intelligence: A Comparative Study of Computing Devices for Deployment of Object Detection Algorithm" @default.
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- W4383746592 doi "https://doi.org/10.1109/incet57972.2023.10170620" @default.
- W4383746592 hasPublicationYear "2023" @default.
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