Women in AI Series 2025 - Distributed Machine Learning for FPGAs in the Cloud: Dr. Miriam Leeser

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Distributed Machine Learning for FPGAs in the Cloud
Machine Learning (ML) is a growing area in both research and applications. Trends
include larger and larger ML models and the interest in getting results from ML with low
latency and high throughput. To address these trends, researchers are increasingly
looking at accelerators (such as Graphics Processing Units (GPUs) and Field
Programmable Gate Arrays (FPGAs), especially those that are directly connected to the
network to achieve low latency access to data.
In this talk, I will introduce the Open Cloud Testbed (OCT): https://octestbed.org/
OCT is available to researchers who are interested in conducting cloud research with
accelerators. We provide GPUS, FPGAs, and AI engines from AMD. The FPGAs and
AI engines are directly connected to the network.
I will discuss experiments on using OCT for distributed ML using multiple network
connected FPGAs. Specifically I will present results for running Resnet50 inference on
the imagenet dataset.
No hardware knowledge is assumed for this webinar.



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  • Date: 22 Apr 2025
  • Time: 11:00 PM UTC to 12:00 AM UTC
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  • Starts 06 March 2025 05:00 AM UTC
  • Ends 22 April 2025 11:30 PM UTC
  • No Admission Charge


  Speakers

Miriam of Northeastern University

Biography:

Miriam Leeser is a Professor of Computer Engineering at Northeastern University and
head of the Reconfigurable Computing Laboratory. Her main research focus is in
hardware accelerators, especially FPGAs.  She has conducted research in floating
point implementations, unsupervised learning, medical imaging, and privacy preserving
data processing. Throughout her career, she has been funded by both government
agencies and companies, including AMD, DARPA, NSF, Google, and MathWorks. She
is the recipient of a Fulbright Scholar Award and a Charter Member of the IEEE
Computer Society Distinguished Contributor Recognition Program.  She is a senior
member of ACM, IEEE and SWE.  Her current research focus is on FPGAs for wireless
communications, FPGAs in the data center, and accelerating machine learning
applications.