Paper Info
Title
AI Tax: The Hidden Cost of AI Data Center Applications
Abstract
AbstractArtificial 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 article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (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 machine learning (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.
Year
DOI
Venue
2021
10.1145/3440689
ACM Transactions on Computer Systems
Keywords
DocType
Volume
AI tax, end-to-end AI application
Journal
37
Issue
ISSN
Citations 
1-4
0734-2071
0
PageRank 
References 
Authors
0.34
6
12
Name
Order
Citations
PageRank
Daniel Richins1162.40
Dharmisha Doshi200.34
Matthew Blackmore300.34
Aswathy Thulaseedharan Nair400.34
Neha Pathapati500.34
Ankit Patel600.34
Brainard Daguman700.34
Daniel Dobrijalowski800.34
Ramesh Illikkal900.34
Kevin Long1000.34
David Zimmerman1100.34
Vijay Janapa Reddi122931140.26