Speaker: Jian Weng King Abdullah University of Science and Technology (KAUST)
Time: 2024-12-17 14:30-2024-12-17 15:30
Venue: RM 1-202, FIT Building (https://meeting.tencent.com/dm/gjjDwcohHgfk)

Abstract:

Transistor scaling not only offers more and more on-chip resources to improve the chip performance, but also urges system developers to design and implement better computer architectures to better utilize these resources. However, existing architectural design and implementation approaches still rely on separate codebases for both simulation-based design decision modeling, and RTL implementation. Jian Weng’s research view these technical challenges in a view of spatial dataflow, and proposes a brand-new abstraction for unified design and implementation. By taking advantage of a widely existing programming paradigm in software engineering, a concise, expressive, and efficient hardware design and implementation framework is proposed. This approach achieves near handcraft performances, while saving 64% LoC. Moreover, Jian Weng is going to apply the spatial dataflow methodology on a next-generation ML inference system by taking advantage of the larger and larger on-chip memory to amortize the memory bottleneck.

Short Bio:

Jian Weng is an Assistant Professor at Computer Science Program of King Abdullah University of Science and Technology (KAUST). His research interests mainly lie on designing, and analyzing specialized accelerators, as well as their associated software stacks. Jian Weng earned his Ph.D. degree from University of California, Los Angeles in 2023 under the guidance of Prof. Tony Nowatzki.