Choosing the Best Hardware for Deep Learning

In today's fast-paced world of machine learning, neural networks have become more computationally intensive, making Machine Learning (ML) implementation on embedded systems increasingly challenging. This paper explores a comparative analysis of Anders embedded platforms and third-party accelerators, focusing on the efficiency, performance and cost of deploying the YOLOv5s model -  a renowned deep learning model for object detection on embedded platforms.

It compares CPUs, ARM architectures, and AI accelerators including Coral TPU and Hailo-8, offering insights for optimising ML deployments.

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