Shewu worked on optimizing memory management for multi-context workloads in the pytorch/executorch repository, focusing on the Qualcomm AI Engine’s spill fill buffer sizing. By refining the maximum spill fill buffer setting using C++ and Python, Shewu addressed resource utilization and stability challenges during concurrent model execution. The technical approach involved tuning buffer parameters to balance memory pressure and throughput, validated through representative multi-context workloads. This targeted feature improved scalable inference performance and efficiency for deep learning applications. The work demonstrated depth in AI optimization and memory management, aligning the system’s behavior with performance goals for demanding, concurrent AI inference scenarios.