AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
Opportunities and challenges in accelerating large-scale search using NVM-based in-memory computing
- Resource-aware ML
Abstract: Large-scale search plays a crucial role in a wide range of machine learning applications, where achieving fast and energy-efficient search is highly desirable. However, accelerating these operations presents significant challenges due to the sheer data scale and inherently irregular memory access patterns. In-memory computing, such as non-volatile memory (NVM)-based content addressable memory (CAM), has emerged as a promising solution for efficient search acceleration, offering the potential for high-speed, parallel lookups while reducing energy consumption. Yet, selecting the most suitable NVM devices and architectures for CAM-based accelerators remains a complex challenge.
This talk explores how architectural and system-level choices impact the efficiency, scalability, and performance of CAM-based search accelerators. It discusses key design challenges, trade-offs, and representative solutions. By connecting advances across devices, circuits, and systems, the talk provides practical insights for researchers and designers developing next-generation search accelerators that can efficiently meet the demands of increasingly large and complex search workloads.
Prof. Dr. Xiaobo Sharon Hu
Xiaobo Sharon Hu is Leo E. and Patti Ruth Linbeck Professor of Engineering in the department of Computer Science and Engineering at the University of Notre Dame, USA. Her research interests include low-power and reliable system design, circuit and architecture design with emerging technologies, real-time embedded systems, and hardware-software co-design. She has published more than 500 papers in these areas and received best paper awards from top design automation conferences. She served as the General Chair and/or TPC Chair of Design Automation Conference, Real-Time Systems Symposium, Embedded Systems Week, etc. She was the Editor-in-Chief of ACM Transactions on Design Automation of Electronic Systems and served as Associate Editor of other ACM and IEEE journals. Sharon Hu is a Fellow of the ACM and the IEEE.
Past Events
Opportunities and challenges in accelerating large-scale search using NVM-based in-memory computing
- Resource-aware ML
Abstract: Large-scale search plays a crucial role in a wide range of machine learning applications, where achieving fast and energy-efficient search is highly desirable. However, accelerating these operations presents significant challenges due to the sheer data scale and inherently irregular memory access patterns. In-memory computing, such as non-volatile memory (NVM)-based content addressable memory (CAM), has emerged as a promising solution for efficient search acceleration, offering the potential for high-speed, parallel lookups while reducing energy consumption. Yet, selecting the most suitable NVM devices and architectures for CAM-based accelerators remains a complex challenge.
This talk explores how architectural and system-level choices impact the efficiency, scalability, and performance of CAM-based search accelerators. It discusses key design challenges, trade-offs, and representative solutions. By connecting advances across devices, circuits, and systems, the talk provides practical insights for researchers and designers developing next-generation search accelerators that can efficiently meet the demands of increasingly large and complex search workloads.
Prof. Dr. Xiaobo Sharon Hu
Xiaobo Sharon Hu is Leo E. and Patti Ruth Linbeck Professor of Engineering in the department of Computer Science and Engineering at the University of Notre Dame, USA. Her research interests include low-power and reliable system design, circuit and architecture design with emerging technologies, real-time embedded systems, and hardware-software co-design. She has published more than 500 papers in these areas and received best paper awards from top design automation conferences. She served as the General Chair and/or TPC Chair of Design Automation Conference, Real-Time Systems Symposium, Embedded Systems Week, etc. She was the Editor-in-Chief of ACM Transactions on Design Automation of Electronic Systems and served as Associate Editor of other ACM and IEEE journals. Sharon Hu is a Fellow of the ACM and the IEEE.
Opportunities and challenges in accelerating large-scale search using NVM-based in-memory computing
- Resource-aware ML
Abstract: Large-scale search plays a crucial role in a wide range of machine learning applications, where achieving fast and energy-efficient search is highly desirable. However, accelerating these operations presents significant challenges due to the sheer data scale and inherently irregular memory access patterns. In-memory computing, such as non-volatile memory (NVM)-based content addressable memory (CAM), has emerged as a promising solution for efficient search acceleration, offering the potential for high-speed, parallel lookups while reducing energy consumption. Yet, selecting the most suitable NVM devices and architectures for CAM-based accelerators remains a complex challenge.
This talk explores how architectural and system-level choices impact the efficiency, scalability, and performance of CAM-based search accelerators. It discusses key design challenges, trade-offs, and representative solutions. By connecting advances across devices, circuits, and systems, the talk provides practical insights for researchers and designers developing next-generation search accelerators that can efficiently meet the demands of increasingly large and complex search workloads.
Prof. Dr. Xiaobo Sharon Hu
Xiaobo Sharon Hu is Leo E. and Patti Ruth Linbeck Professor of Engineering in the department of Computer Science and Engineering at the University of Notre Dame, USA. Her research interests include low-power and reliable system design, circuit and architecture design with emerging technologies, real-time embedded systems, and hardware-software co-design. She has published more than 500 papers in these areas and received best paper awards from top design automation conferences. She served as the General Chair and/or TPC Chair of Design Automation Conference, Real-Time Systems Symposium, Embedded Systems Week, etc. She was the Editor-in-Chief of ACM Transactions on Design Automation of Electronic Systems and served as Associate Editor of other ACM and IEEE journals. Sharon Hu is a Fellow of the ACM and the IEEE.