Full-Stack Collaboration for Robust Heterogeneity-Enabled AI Systems
In this new era of AI with diverse hardware accelerators such as GPUs and quantum circuits, achieving system-wide robustness requires tackling issues throughout all system layers, spanning from software applications to hardware components. My research is to enhance the robustness of heterogeneity-enabled AI systems by reinventing software testing and analysis techniques via leveraging full-stack insights and advanced AI capabilities. I have completed one research project and have collaborated on a couple of others at the application and language levels. As the next steps, I will explore (1) holistic regression testing to prioritize test inputs associated with system-wide changes and (2) full-stack analysis to optimize computing resource allocation and reduce hardware reliance by analyzing application characteristics and using alternative resources in tandem.
Tue 22 OctDisplayed time zone: Pacific Time (US & Canada) change
11:00 - 12:30 | |||
11:00 30mTalk | Full-Stack Collaboration for Robust Heterogeneity-Enabled AI Systems Doctoral Symposium Yuxin Qiu University of California at Riverside | ||
11:30 30mTalk | JMVX: Improving Record-Replay for Managed Languages Doctoral Symposium David Schwartz University of Illinois at Chicago | ||
12:00 30mTalk | Unified Analysis Techniques for Programs with Outcomes Doctoral Symposium Noam Zilberstein Cornell University |