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1、Deployment of AI in Mobile Computing and Edge DevicesAbstract Artificial intelligence(AI)has emerged as a critical force in the technology industry,improving efficiency,productivity,and decision-making across a wide range of sectors.The application of AI is rapidly spreading beyond desktop and cloud

2、 computing,offering not only performance benefits but also substantial cost-saving potential.Often overlooked domains are the integration of AI in mobile computing and edge devices.Examples include Advanced Driver Assistance Systems in autonomous vehicles,mapping capabilities in robotic vacuum clean

3、ers,biometric data analysis in wearable fitness trackers,and natural language processing in smart home devices like Alexa or Google Home.The advantages of using AI on edge devices,include the portability and scalability of their usage in many industries,but also reduced latency,and privacy.However,i

4、t is challenging to implement because of the limited computational power and memory,few developers experienced in AI optimization for low-power devices,and the selection of the right hardware and models to use.In this paper,we offer solutions for what to do when faced with the challenges of deployme

5、nt of AI in embedded devices,and lead by example on how we overcame these challenges for our specific edge device deployment.Our edge deployment uses a YOLOv8 nano model on a Jetson Nano(reComputer J1020v2,4GB)for object detection in a factory setting.The system achieves 75%mAP(mean Average Precisio

6、n)while processing up to 7 full HD images per second,demonstrating that low-power edge devices can deliver reliable,real-time AI performance in practical applications.Index TermsJetson,Yolo,Object Detection,Edge Computing,AI.WHITEPAPER DEPLOYMENT OF AI IN MOBILE COMPUTING AND EDGE DEVICESArtificial