New Frontiers in Large Scale AI Infrastructure Builds | Kisaco Research

In this keynote, Dr. Cédric Bourrasset, AI Distinguished Expert at Atos, will reveal how Atos pioneered the successful architecture, build, and delivery of large-scale AI infrastructures. He will present a live demonstration of Atos-driven technology to illustrate new AI-driven endpoints featuring GPU and IPU workflow capabilities, featuring a global customer case study to elaborate on the current complex challenges faced by designing and manufacturing large-scale AI computing platforms. He will also leverage over 15 years of personal experience in designing and manufacturing supercomputing systems.

Session Topics: 
Developer Efficiency
Edge AI
Enterprise AI
ML at Scale
Novel AI Hardware
Systems Design
Sponsor(s): 
Atos
Speaker(s): 

Author:

Cedric Bourrasset

Head of High Performance AI Business Unit
Atos

Dr. Cedric Bourrasset is AI Business Leader for High Performance Computing Business Unit at Atos. He is also AI product manager for the Atos Codex AI suite, software enabling AI workloads into HPC environments as well as integrating a computer vision solution. He joined Atos in 2016 as an expert in the HPC/AI domain.

Previously, Cedric received his Ph.D. in Electronics and computer vision from the Blaise Pascal University of Clermont-Ferrand defending the dataflow model of computation for FPGA High Level Synthesis problematic in embedded machine learning applications.

Cedric Bourrasset

Head of High Performance AI Business Unit
Atos

Dr. Cedric Bourrasset is AI Business Leader for High Performance Computing Business Unit at Atos. He is also AI product manager for the Atos Codex AI suite, software enabling AI workloads into HPC environments as well as integrating a computer vision solution. He joined Atos in 2016 as an expert in the HPC/AI domain.

Previously, Cedric received his Ph.D. in Electronics and computer vision from the Blaise Pascal University of Clermont-Ferrand defending the dataflow model of computation for FPGA High Level Synthesis problematic in embedded machine learning applications.