Maximizing Server Efficiency: from microarchitecture to machine-learning accelerators [SystemX Seminar]

Maximizing Server Efficiency: from microarchitecture to machine-learning accelerators
Tuesday, October 3, 2017 - 4:30pm
Huang 018
Mike Ferdman (Stony Brook University)
Abstract / Description: 

Datacenter servers have emerged as the predominant computing platform, impacting nearly every aspect of our daily lives. The need to study, understand, and improve server systems has been the main driver of my research career. I will begin this talk with a brief overview of our work on the CloudSuite benchmarks and Dark Silicon in Servers, which identified a number of challenges and opportunities for server architecture. Then, I will describe how these results have led to our work on Scale-Out Processors and Temporal Memory Streaming, and dive deeper into my students' recent results on instruction sequence memorization and FPGA-based accelerators for machine learning.


Mike Ferdman is an Assistant Professor of Computer Science at Stony Brook University, where he co-directs the Computer Architecture Stony Brook (COMPAS) Lab. His research interests are in the area of computer architecture, with particular emphasis on the server computing stack. His current projects center on FPGA accelerators for machine learning, emerging memory technologies, and speculative micro-architectural techniques. Mike received a BS in Computer Science, and BS, MS, and PhD in Electrical and Computer Engineering from Carnegie Mellon University.