[Systems talk]: "The Different Scales of Resource-Aware ML & How to Tackle Them "
Event details
| Date | 08.12.2023 |
| Hour | 12:00 › 13:00 |
| Speaker | Pinar Tözün, Associate Professor, IT University of Copenhagen. |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
Today, machine learning (ML) runs at various scales of hardware resources from the cloud and high-performance computing (HPC) centers to edge and Internet-of-Things (IoT) devices. To achieve resource-aware machine learning, we must understand the needs and challenges of ML applications at these different scales. In this talk, we will first investigate ways of improving hardware utilization on modern and powerful CPU-GPU co-processors, which serve as the commodity hardware for ML in the cloud and HPC, using workload collocation. Then, we will investigate performance and power trade-offs for ML-based image analysis in space using resource-constrained edge/IoT devices.
Bio:
Pinar Tözün, is an Associate Professor at IT University of Copenhagen. Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her thesis received the ACM SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2016. Her research focuses on hardware-conscious machine learning, performance characterization of data-intensive systems, and scalability and efficiency of data-intensive systems on modern hardware.
Today, machine learning (ML) runs at various scales of hardware resources from the cloud and high-performance computing (HPC) centers to edge and Internet-of-Things (IoT) devices. To achieve resource-aware machine learning, we must understand the needs and challenges of ML applications at these different scales. In this talk, we will first investigate ways of improving hardware utilization on modern and powerful CPU-GPU co-processors, which serve as the commodity hardware for ML in the cloud and HPC, using workload collocation. Then, we will investigate performance and power trade-offs for ML-based image analysis in space using resource-constrained edge/IoT devices.
Bio:
Pinar Tözün, is an Associate Professor at IT University of Copenhagen. Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her thesis received the ACM SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2016. Her research focuses on hardware-conscious machine learning, performance characterization of data-intensive systems, and scalability and efficiency of data-intensive systems on modern hardware.
Practical information
- Informed public
- Free
Organizer
- Prof. Anastasia Ailamaki