Seok-Bum Ko of University of Saskatchewan

Deep learning can provide superior performance in many fields of applications. However, the cost of implementing deep learning models in practical applications is expensive. Deep learning models are both computation intensive and memory intensive. Computation is an important aspect for deep learning. It can determine the latency that is how fast the results can be obtained. In this seminar, computer arithmetic for deep learning will be discussed. This lecture will start with discussing the computation requirements of deep learning models and layers. Then, several computer arithmetic designs for deep learning in the literature will be used as examples. Finally, future trends of computer arithmetic for deep learning computation will be discussed.

Biography:

Seokbum Ko is currently a Professor at the Department of Electrical and Computer Engineering and the Division of Biomedical Engineering, University of Saskatchewan, Canada. He received his PhD from the University of Rhode Island, USA in 2002 .His areas of research interest include computer architecture/arithmetic, efficient hardware implementation of compute-intensive applications, deep learning processor architecture and biomedical engineering. He is an IEEE Circuits and Systems Society Distinguished Lecturer (2024-2025), a senior member of IEEE circuits and systems society and an associate editor for IEEE TVLSI, IEEE TCAS-II, IEEE Access and IET Computers & Digital Techniques. He is an active member of IEEE CAS Technical Committee, IEEE P3109, IEEE754-2029, IEEE Domain-Specific Accelerators Standards Committee and IEEE Emerging Processor Systems Standards Committee. He was an associate editor for IEEE TCASI (2019-2021).

Address:University of Saskatchewan, , Saskatchewan, Canada