About

Seda Ogrenci is a Professor of Electrical and Computer Engineering and a Professor of Computer Science at Northwestern University. She holds a PhD in Computer Science from the University of California, Los Angeles, an MS in Electrical and Computer Engineering from Northwestern University, and a BS in Electrical and Electronic Engineering from Bogazici University in Istanbul, Turkey. Her research interests include design automation, real-time edge AI/ML for science, thermal aware design of circuits and systems, thermal sensing and cooling systems for high performance systems, and power and energy aware memory systems. She has contributed to the field through various publications and research projects, focusing on advanced computing systems, thermal management, and machine learning applications in science.

Research topics

• Computer Science
• Artificial Intelligence
• Electronic engineering
• Operating system
• Computer architecture
• Embedded system
• Computer hardware
• Engineering
• Telecommunications

Selected publications

• A High Level Synthesis Methodology for Dynamic Monitoring of FPGA ML Accelerators

  2024 · 4 citations

  • Computer Science
  • Computer Science
  • Embedded system

  In this paper, we present concepts towards a HLS-driven dynamic monitoring and debugging framework. Traditionally, in-situ debugging and dynamic monitoring is accessible during the early design stages through costly co-simulation cycles and through invasive tools and interfaces. We propose a methodology where dynamic monitoring is embedded into the high level synthesis description of machine learning (ML) accelerators within the open source hls4ml tool. We discuss the usage of the framework for monitoring FIFO channel utilization, which is a critical structure utilized to implement streaming based ML accelerators on FPGAs.

  DOI

• In-pixel AI for lossy data compression at source for X-ray detectors

  Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2023 · 8 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherOA PDFDOI

• Applications and Techniques for Fast Machine Learning in Science

  Frontiers in Big Data · 2022 · 73 citations

  • Computer Science
  • Computer Science
  • Data science

  machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

  DOI

Frequent coauthors

• Majid Sarrafzadeh

  4 shared

• Kia Bazargan

  University of Minnesota

  3 shared

• Giuseppe Di Guglielmo

  2 shared

• Nhan Viet Tran

  Fermi National Accelerator Laboratory

  2 shared

• Farah Fahim

  Fermi National Accelerator Laboratory

  2 shared

• Adam Quinn

  Fermi National Accelerator Laboratory

  1 shared

• Priyanka Dilip

  1 shared

• Danny Noonan

  Fermi National Accelerator Laboratory

  1 shared

Similar researchers at Northwestern University

• Mercouri G. Kanatzidish-205
• Chad Mirkinh-189
• Omar Farhah-184
• Tobin J. Marksh-176
• Joseph Hupph-171