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Many real-world applications are widely adopting the edge computing paradigmdue to its low latency and better privacy protection. With notable success inAI and deep learning (DL), edge devices and AI accelerators play a crucial rolein deploying DL inference services at the edge of the Internet. While priorworks quantified various edge devices' efficiency, most studies focused on theperformance of edge devices with single DL tasks. Therefore, there is an urgentneed to investigate AI multi-tenancy on edge devices, required by many advancedDL applications for edge computing.  This work investigates two techniques - concurrent model executions anddynamic model placements - for AI multi-tenancy on edge devices. With imageclassification as an example scenario, we empirically evaluate AI multi-tenancyon various edge devices, AI accelerators, and DL frameworks to identify itsbenefits and limitations. Our results show that multi-tenancy significantlyimproves DL inference throughput by up to 3.3x -- 3.8x on Jetson TX2. These AImulti-tenancy techniques also open up new opportunities for flexible deploymentof multiple DL services on edge devices and AI accelerators.

AI Multi-Tenancy on Edge: Concurrent Deep Learning Model Executions and Dynamic Model Placements on Edge Devices