  Artificial intelligence and machine learning are experiencing widespread
adoption in industry and academia. This has been driven by rapid advances in
the applications and accuracy of AI through increasingly complex algorithms and
models; this, in turn, has spurred research into specialized hardware AI
accelerators. Given the rapid pace of advances, it is easy to forget that they
are often developed and evaluated in a vacuum without considering the full
application environment. This paper emphasizes the need for a holistic,
end-to-end analysis of AI workloads and reveals the "AI tax." We deploy and
characterize Face Recognition in an edge data center. The application is an
AI-centric edge video analytics application built using popular open source
infrastructure and ML tools. Despite using state-of-the-art AI and ML
algorithms, the application relies heavily on pre-and post-processing code. As
AI-centric applications benefit from the acceleration promised by accelerators,
we find they impose stresses on the hardware and software infrastructure:
storage and network bandwidth become major bottlenecks with increasing AI
acceleration. By specializing for AI applications, we show that a purpose-built
edge data center can be designed for the stresses of accelerated AI at 15%
lower TCO than one derived from homogeneous servers and infrastructure.
