Speaker: Jian Weng King Abdullah University of Science and Technology (KAUST)
Time: 2023-11-15 11:00-2023-11-15 12:00
Venue: FIT 1-222

Abstract:

In reaction to the waning benefit of transistor scaling and the increasing demands on computing power, specialized accelerators have drawn significant attention from both academics and industry because of their orders-of-magnitude performance improvement and energy efficiency. All these accelerators require non-trivial human efforts, from designing the architecture to having a full-stack implementation. Therefore, the software/hardware co-designed innovations are often monopolized by several large teams in large companies. In this talk, I will first discuss how my research democratizes the accelerator designs and unifies the hardware/software innovations by automating the accelerator design process under a unified programming paradigm. By taking advantage of the compiler’s awareness of the program behaviors that profit from hardware specialization, accelerators can be automatically synthesized by searching through a well-defined design space. These automatically designed accelerators achieve comparable cost/performance efficiency compared with prior handcrafted designs. In the rest of the talk, I will also cover how this work inspires me to develop techniques to accelerate emerging application domains by orders-of-magnitude speedup, including digital signal processing and DNN inference,and how I take advantage of this work to revolutionize the FPGA programming paradigm.

Short Bio:

Jian Weng earned his PhD degree from University of California, Los Angeles (UCLA) in Sep of 2023, and will be joining King Abdullah University of Science and Technology (KAUST) as an Assistant Professor this November. His research interests span designing and analyzing specialized hardware accelerators, as well as their associated software/hardware interfaces and compilation techniques. His works have been accepted by HPCA (CCF-A), ISCA (CCF-A), MICRO (CCF-A), and CGO (CCF-B), and were awarded as IEEE Micro Top Picks, and Best Paper Runner-up.