Keynote

Architectures for AI

Steven K. Reinhardt

Senior Fellow, AMD

Abstract: Deep Neural Networks (DNNs), especially those powering Generative AI models, are revolutionizing computing and driving unprecedented investments in infrastructure. While DNNs are more challenging than traditional workloads in their immense demand for compute and memory bandwidth, they also provide new opportunities for optimization due to their high-level tensor dataflow construction and tolerance for approximation. Additionally, the DNN workload landscape is highly diverse, spanning distinctions like training vs. inference, datacenter vs. client deployment, and prompt vs. token processing. This variability has spurred the adoption of a wide array of computing architectures, including GPUs, custom ASICs, SoC-integrated accelerators, CPUs, and FPGAs. In this talk, I will share insights from deploying DNN solutions across several of these architectures, discuss some current work, and highlight challenges and opportunities for future architecture development.

Speaker: Steven K. Reinhardt is a Senior Fellow in the AI Group at AMD. He returned to AMD in 2023 after spending seven years at Microsoft managing a team doing DNN inference on FPGAs for Bing. In his previous stint at AMD, he spent 8½ years at AMD Research, primarily on exascale projects. Prior to that, he was on the faculty at the University of Michigan. Steve is an IEEE Fellow and an ACM Distinguished Scientist. He has a PhD from the University of Wisconsin, an MS from Stanford, and a BS from Case Western Reserve University.