Invited Talk: “Scaling CNN Inference for Extreme Throughput”
Performance scaling with traditional computing architectures becomes increasingly challenging as next generation technology nodes provide diminishing benefits. Semiconductor companies aim to unleash new levels of performance through further specialization of compute and memory subsystems for specific application domains.
During this talk, we will discuss examples of extreme forms of specialization that help scaling CNN inference to 100s of millions of inputs/second to handle ML workloads in novel applications such as network intrusion detection.
Speakers, Panelists and Moderators
MICHAELA BLOTTDistinguished EngineerXilinxMichaela Blott is a Distinguished Engineer at Xilinx Research in Dublin, Ireland, where she heads a team of international scientists driving exciting research to define new application domains for Xilinx devices, such as machine learning, in both embedded and hyperscale deployments. She earned her Master’s degree from the University of Kaiserslautern in Germany and brings over 25 years of leading edge computer architecture and advanced FPGA and board design, in research institutions (ETH Zurich and Bell Labs) and development organizations. She is heavily involved with the international research community serving as the technical co-chair of FPL’2018, DATE’2020, workshop organizer (H2RC), industry advisor on numerous EU projects, and member of numerous technical program committees (FPL, ISFPGA, DATE, etc.) and most recently received the Women in Tech Award 2019.