Intelligent Software in the Era of Deep Learning

Speaker: Yuke Wang
         Department of computer Science
         University of California, Santa Barbara (UCSB)

Title:  "Intelligent Software in the Era of Deep Learning"

Date:   Thursday, 1 February 2024

Time:   10:00 am - 11:00 am (Hong Kong Local Time)

Zoom link:

Meeting ID: 966 8851 6988
Passcode: 202425


With the end of Moore's Law and the rise of compute- and data-intensive
deep-learning (DL) applications, the focus on arduous new processor design
has shifted towards a more effective and agile approach -- Intelligent
Software to maximize the performance gains of DL hardware like GPUs.

In this talk, I will first highlight the importance of software innovation
to bridge the gap between the increasingly diverse DL applications and the
existing powerful DL hardware platforms. The second part of my talk will
recap my research work on DL system software innovation, focusing on
bridging the 1) Precision Mismatch between DL applications and
high-performance GPU units like Tensor Cores (PPoPP '21 and SC '21), and
2) Computing Pattern Mismatch between the sparse and irregular DL
applications such as Graph Neural Networks and the dense and regular
tailored GPU computing paradigm (OSDI '21 and OSDI '23). Finally, I will
conclude this talk with my vision and future work for building efficient,
scalable, and secure DL systems.


Yuke Wang is a final-year Doctor of Philosophy (Ph.D.) candidate in the
Department of computer science at the University of California, Santa
Barbara (UCSB). He got his Bachelor of Engineering (B.E.) in software
engineering from the University of Electronic Science and Technology of
China (UESTC) in 2018. At UCSB, Yuke is working with Prof.Yufei Ding (Now
at UC at San Diego, CSE). Yuke's research interests include Systems &
Compiler for Deep Learning and GPU-based High-performance Computing. His
projects cover graph neural network (GNN) optimization and its
acceleration on GPUs. Yuke's research has resulted in 20+ publications
(with 10 first-authored papers) in top-tier conferences, including OSDI,
ASPLOS, ISCA, USENIX ATC, PPoPP, and SC. Yuke's research outcome has been
adopted for further research in industries (e.g., NVIDIA, OctoML, and
Alibaba) and academia (e.g., University of Washington and Pacific
Northwest National Laboratory). Yuke is also the recipient of the NVIDIA
Graduate Fellowship 2022 (Top-10 out of global applicants) and has
industry experience at Microsoft Research, NVIDIA Research, and Alibaba.
The ultimate goal of Yuke's research is to facilitate efficient, scalable,
and secure deep learning in the future.