
Over four months, Tom Jamula enhanced the qualcomm-linux/meta-qcom repository by integrating the Qualcomm AI Runtime SDK and enabling Hexagon hardware acceleration, supporting efficient machine learning inference across multiple boards. He upgraded and refactored gst-plugins-imsdk, improving GStreamer pipeline stability and modularity for multimedia and AI workloads. Tom addressed build system reliability and packaging compatibility, using CMake and Python to streamline deployment and dependency management. His work included packaging architecture-specific DSP libraries and decoupling open-source and proprietary components, resulting in faster builds and clearer image composition. These contributions demonstrated depth in embedded systems, backend development, and performance optimization for Linux platforms.
Month: 2026-04 — Delivered major Gst-plugins-imsdk upgrades and packaging refinements for qualcomm-linux/meta-qcom, focusing on stability, pipeline correctness, and modular buildability. Key improvements include decoupled base/proprietary components, expanded format support, and clearer OSS vs proprietary dependencies, enabling cleaner image composition and faster builds.
Month: 2026-04 — Delivered major Gst-plugins-imsdk upgrades and packaging refinements for qualcomm-linux/meta-qcom, focusing on stability, pipeline correctness, and modular buildability. Key improvements include decoupled base/proprietary components, expanded format support, and clearer OSS vs proprietary dependencies, enabling cleaner image composition and faster builds.
March 2026: Delivered Hexagon ML acceleration enablement on iq-615-evk by packaging architecture-specific DSP libraries for QAIRT and enabling the QAIRT hexagon package in the machine configuration, enabling hardware-accelerated ML workloads and strengthening Qualcomm AI Runtime integration. This work provides a foundation for faster ML inference on iq-615-evk and improved energy efficiency.
March 2026: Delivered Hexagon ML acceleration enablement on iq-615-evk by packaging architecture-specific DSP libraries for QAIRT and enabling the QAIRT hexagon package in the machine configuration, enabling hardware-accelerated ML workloads and strengthening Qualcomm AI Runtime integration. This work provides a foundation for faster ML inference on iq-615-evk and improved energy efficiency.
February 2026: Delivered key features and stability improvements across qualcomm-linux/meta-qcom and pytorch/executorch, focusing on packaging reliability, build compatibility, and enabling GenAI capabilities. This month’s work reduces risk in QAIRT SDK packaging, expands APIs and datatype support, and ensures forward-compatibility with updated dependencies.
February 2026: Delivered key features and stability improvements across qualcomm-linux/meta-qcom and pytorch/executorch, focusing on packaging reliability, build compatibility, and enabling GenAI capabilities. This month’s work reduces risk in QAIRT SDK packaging, expands APIs and datatype support, and ensures forward-compatibility with updated dependencies.
In January 2026, delivered end-to-end Qualcomm AI Runtime (QAIRT) integration and Hexagon support within qualcomm-linux/meta-qcom, enabling efficient ML inference across CPU, GPU, and NPU/HTP accelerators and across multiple boards. Added QAIRT recipe and Hexagon package enablement in machine configurations. This work lays the groundwork for faster model deployment via IMSDK and improves cross-device ML performance.
In January 2026, delivered end-to-end Qualcomm AI Runtime (QAIRT) integration and Hexagon support within qualcomm-linux/meta-qcom, enabling efficient ML inference across CPU, GPU, and NPU/HTP accelerators and across multiple boards. Added QAIRT recipe and Hexagon package enablement in machine configurations. This work lays the groundwork for faster model deployment via IMSDK and improves cross-device ML performance.

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