Manya Ghobadi is faculty in the EECS department at MIT. Her research spans
different areas in computer networks, focusing on optical reconfigurable
networks, networks for machine learning, and high-performance cloud
infrastructure. Her work has been recognized by the ACM-W Rising Star
award, Sloan Fellowship in Computer Science, ACM SIGCOMM Rising Star
award, NSF CAREER award, Optica Simmons Memorial Speakership award, best
paper award at the Machine Learning Systems (MLSys) conference, as well as
the best dataset and best paper awards at the ACM Internet Measurement
Conference (IMC). Manya received her Ph.D. from the University of Toronto
and spent a few years at Microsoft Research and Google before joining MIT.

AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

AG Audience

English

60 Minutes

Kaiserslautern

In this talk, I will explore three elements of designing next-generation
machine learning systems: congestion control, network topology, and
computation frequency. I will show that fair sharing, the holy grail of
congestion control algorithms, is not necessarily desirable for deep
neural network training clusters. Then I will introduce a new optical
fabric that optimally combines network topology and parallelization
strategies for machine learning training clusters. Finally, I will
demonstrate the benefits of leveraging photonic computing systems for
real-time, energy-efficient inference via analog computing. I will discuss
that pushing the frontiers of optical networks for machine learning
workloads will enable us to fully harness the potential of deep neural
networks and achieve improved performance and scalability.

Vera Schreiber

+49 631 9303 9603

--email hidden

passcode not visible

see notes