Requirements for an Enterprise AI Benchmark

(Update to a recent talk of note given on 8/23/2018)

Slides
Talk recording.

ISSIP Cognitive Systems Institute Group Webinar

full series here http://cognitive-science.info/community/weekly-update/

Talk Title: Requirements for an Enterprise AI Benchmark

Speakers: Cedric Bourrasset, Atos Bull; Rajesh Bordawekar, IBM

Talk Description:  

At present, AI benchmarks either focus on evaluating deep learning approaches or infrastructure capabilities. These approaches don’t capture end-to-end performance behavior of enterprise AI workloads. It is also clear that there is not one reference metric that will be suitable for all AI applications nor all existing platforms. Cedric and Rajesh first present the state of the art regarding the current basic and most popular AI benchmarks. They then present the main characteristics of AI workloads from various industrial domains. Finally, they focus on the needs for ongoing and future industry AI benchmarks and conclude on the gaps to improve AI benchmarks for enterprise workloads.  

Cedric Bourasset : After receiving a Ph.D. in Electronics and computer vision in 2016 from the Blaise Pascal University of Clermont-Ferrand defending the dataflow model of computation for FPGA High Level Synthesis problematic in embedded machine learning application, Cedric is now working as AI Product Manager at Atos Bull with the mission to develop Atos AI product line. One product is a software solution for developing AI enterprise solutions and the other one is computer vision solution for people detection, tracking and reidentification into multi-camera environments.

Rajesh Bordawekar:  Rajesh is a member of the Systems Acceleration department at the IBM T. J. Watson Research Center. Prior to joining IBM Research in September 1998, he was a post-doctoral fellow at the Center for Advanced Computing Research, California Institute of Technology. 
He received his PhD in Computer Engineering from Syracuse University. 
Rajesh studies interactions between applications, programming languages/runtime systems, and computer architectures.    He is interested in understanding how modern hardware, multi-core processors, GPUs, and SSDs impact design of optimal algorithms for main memory and out-of-core problems. .... 

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