PDL Talk Series
August 20, 2026
TIME: 12:00 noon to approximately 1:00 pm EDT
PLACE: Virtual - a zoom link will be emailed closer to the seminar
Olivia Hsu, Carnegie Mellon University
Programming Data-dependent Applications on Hardware Accelerators
Modern workloads, including machine learning and data analytics, are increasingly data-dependent. Their execution depends heavily on sparsity, dynamic control flow, irregular communication, and evolving runtime behavior. At the same time, computer architecture is rapidly shifting toward heterogeneous, domain-specific hardware, which include hardware accelerators, reconfigurable dataflow architectures, and decoupled access-execute systems. This talk discusses the systems challenges that emerge at the intersection of these trends and argues that hardware adoption is increasingly limited not by hardware capability itself, but by a systems ability to support it. I will present my group’s research on hardware–software systems for efficiently executing complex, data-dependent applications on hardware accelerators. The talk covers research directions in my group, including programming systems for sparse and dynamic ML accelerators and compiler/runtime abstractions for irregular computation on dataflow accelerators.
BIO: Olivia Hsu is an Assistant Professor of Electrical and Computer Engineering and, by courtesy, Computer Science at Carnegie Mellon University. Her research lies at the intersection of compilers and computer architecture, spanning programming languages to digital VLSI. Her recent work includes sparse tensor compilation, compilation and mapping for heterogeneous domain-specific accelerators, dataflow abstractions for hardware, and reconfigurable spatial architectures. Her research has been recognized with the 2026 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award, a Distinguished Paper Award at PLDI 2023, and a Best Paper at the Deep-Learning for Code Workshop co-located with ICML 2025. Her honors include the NSF Graduate Research Fellowship, Rising Stars in EECS, IEEE-HKN Alton B. Zerby and Carol M. Korner Outstanding Student Award, and a UC Berkeley Outstanding Graduate Student Instructor Award. Prior to joining Carnegie Mellon University, she was a postdoctoral researcher at Stanford University, where she also earned her Ph.D. and M.S. in Computer Science, advised by Kunle Olukotun and Fredrik Kjolstad. More information can be found at her website: https://cs.stanford.edu/~owhsu.
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