IEEE Lafayette Section Young Professionals Affinity Group - IEEE Day Tech Chat
In this talk, the speaker will first discuss how new mapping solutions, i.e., composing heterogeneous accelerators within a system-on-chip with both FPGAs and AI tensor cores, achieve orders of magnitude energy efficiency gains when compared to monolithic accelerator mapping designs for deep learning applications. Then, the speaker will apply such novel mapping solutions to show how design space explorations are performed to achieve low-latency AI inference. The speaker will further discuss how we applied these techniques to different application domains, including autonomous vehicles, additive manufacturing, etc.
Date and Time
Location
Hosts
Registration
-
Add Event to Calendar
Loading virtual attendance info...
- Starts 07 September 2025 05:00 AM UTC
- Ends 07 October 2025 02:00 PM UTC
- No Admission Charge
Speakers
Dr. Peipei Zhou
Efficient Programming on Heterogeneous Accelerators
Biography:
Peipei Zhou is currently an Assistant Professor at the School of Engineering, Brown University. She received her Ph.D. in Computer Science (2019) and M.S. in Electrical and Computer Engineering (2014) from UCLA, and her B.S. in Electrical and Computer Engineering (2012) from Southeast University. Her research investigates architecture, programming abstraction, and design automation tools for reconfigurable computing and heterogeneous computing. She has published 40 papers in IEEE/ACM computer system and design automation conferences and journals. Her work has won the 2019 IEEE TCAD Donald O. Pederson Best Paper Award. Other awards include the 2024 ACM/IEEE IGSC Best Viewpoint Paper, the 2025 ACM/SIGDA FPGA Best Paper Nominee, the 2018 IEEE ISPASS Best Paper Nominee, and the 2018 IEEE/ACM ICCAD Best Paper Nominee.
Email:
Agenda
Introduction
Talk/Presentation
Q&A
Media
| Copy_of_Copy_of_Carousel_Template_A_1920x1080-3 | 2.31 MiB |