Vaiskv contributed to the CodeLinaro/onnxruntime repository by implementing Qualcomm QMX kernel support within the ONNX Runtime MLAS subsystem. This work enabled SGEMM, QGEMM, and Convolution operations to utilize QMX hardware optimizations, expanding MLAS acceleration capabilities on Qualcomm platforms. Vaiskv approached the task through C++ development and CMake integration, focusing on kernel integration and cross-hardware performance optimization. The feature delivered a foundation for improved inference throughput and broader hardware support, addressing the need for efficient machine learning workloads on Qualcomm devices. The work demonstrated technical depth in ML acceleration backends and disciplined code delivery, with no major bugs reported.