CodeLinaro/onnxruntime
Apr 2025 – Jul 2025
3 Months active
Languages Used
C++Python
Technical Skills
C++ developmentCommand Line ToolsMachine LearningModel OptimizationProtobufPython Scripting
quic-hungjuiw
PROFILE
Quic-hungjuiw
Contributed to the CodeLinaro/onnxruntime repository by developing and optimizing backend features for ONNX model deployment, focusing on both performance and compatibility. Built command-line tools in C++ and Python for static quantization and custom inference result formatting, enabling more efficient model optimization and verifiable outputs. Enhanced the QNN Execution Provider by improving preprocessing pipelines and fixing data type handling for NPU backends, which increased deployment reliability and ONNX compatibility. Addressed hardware-specific issues such as AMD processor identification, ensuring robust cross-platform testing. Emphasized thorough unit testing and debugging throughout, demonstrating depth in backend development, machine learning workflows, and model optimization.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
4
Lines of code
1,621
Activity Months3
Your Network
249 peopleWork History
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for CodeLinaro/onnxruntime focused on delivering cross-backend reliability and hardware-identity correctness to strengthen model execution, testing stability, and business value. Key features/bugs delivered include (1) cross-backend data type validation infrastructure for QNN BatchNorm with processing methods and unit tests, enabling consistent BatchNorm behavior across CPU, HTP, and GPU backends, and (2) a critical AMD processor identification fix in CPUIDInfo, correcting the vendor check from GenuineAMD to AuthenticAMD to ensure AutoEpSelection and OrtEpLibrary tests run reliably on AMD hardware.
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for CodeLinaro/onnxruntime focused on delivering cross-backend reliability and hardware-identity correctness to strengthen model execution, testing stability, and business value. Key features/bugs delivered include (1) cross-backend data type validation infrastructure for QNN BatchNorm with processing methods and unit tests, enabling consistent BatchNorm behavior across CPU, HTP, and GPU backends, and (2) a critical AMD processor identification fix in CPUIDInfo, correcting the vendor check from GenuineAMD to AuthenticAMD to ensure AutoEpSelection and OrtEpLibrary tests run reliably on AMD hardware.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for CodeLinaro/onnxruntime focusing on QNN Execution Provider (EP) improvements. Delivered two critical updates: (1) a bug fix for SFIXED to UFIXED scale transformation in InstanceNorm when using the NPU backend to prevent CPU fallback, with an accompanying unit test validating behavior across multiple input data types; (2) a feature to exclude initializers from model inputs in QNN preprocessing to improve ONNX compatibility. These changes improve deployment reliability for NPU-accelerated workloads and broaden compatibility across ONNX versions. Demonstrates strengths in backend debugging, unit testing, and preprocessing pipeline enhancements, driving stronger product stability and broader device support.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for CodeLinaro/onnxruntime focusing on QNN Execution Provider (EP) improvements. Delivered two critical updates: (1) a bug fix for SFIXED to UFIXED scale transformation in InstanceNorm when using the NPU backend to prevent CPU fallback, with an accompanying unit test validating behavior across multiple input data types; (2) a feature to exclude initializers from model inputs in QNN preprocessing to improve ONNX compatibility. These changes improve deployment reliability for NPU-accelerated workloads and broaden compatibility across ONNX versions. Demonstrates strengths in backend debugging, unit testing, and preprocessing pipeline enhancements, driving stronger product stability and broader device support.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for CodeLinaro/onnxruntime focusing on delivering two core capabilities: saved inference results in a user-specified format for ONNX Test Runner and a CLI tool for static quantization. These changes enable verifiable outputs against custom metrics and help optimize model size and inference speed, aligning with QA automation, performance, and deployment readiness.
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for CodeLinaro/onnxruntime focusing on delivering two core capabilities: saved inference results in a user-specified format for ONNX Test Runner and a CLI tool for static quantization. These changes enable verifiable outputs against custom metrics and help optimize model size and inference speed, aligning with QA automation, performance, and deployment readiness.
April 2025
Activity
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Quality Metrics
Correctness96.6%
Maintainability83.4%
Architecture90.0%
Performance83.4%
AI Usage23.4%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++C++ developmentCommand Line ToolsMachine LearningModel OptimizationProtobufPythonPython ScriptingTesting frameworksbackend developmentdebuggingtestingunit testing
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