microsoft/onnxruntime
Aug 2025 – Sep 2025
2 Months active
Languages Used
C++
Technical Skills
C++C++ developmentDeep LearningMachine LearningNeural NetworksQNN
Mike Hsu
PROFILE
Mike Hsu
During a two-month period, Much Hsu enhanced the QNN Execution Provider in the microsoft/onnxruntime repository, focusing on expanding core operation support and improving model compatibility for attention-based workloads. He implemented features such as 2D bias fusion in Gemm, Mod operation support for edge cases, Thresholded ReLU, and Einsum improvements for QK attention, all using C++ and deep learning techniques. In the following month, he delivered negative padding aligned with ONNX, a 2x2 matrix inverse operation, and flexible TopK attribute handling. His work demonstrated depth in algorithm optimization and neural network operations, addressing both performance and compatibility challenges in production environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
2,038
Activity Months2
Your Network
185 people
Work History
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for microsoft/onnxruntime: Feature-focused delivery on QNN Execution Provider enhancements that broaden compatibility and runtime flexibility. Key features delivered include negative padding support aligned with ONNX, an inverse operation for 2x2 matrices, and enabling the TopK 'largest' attribute without requiring pre-sorted output. These changes were implemented across three commits (hashes: 9d650a4b2348d737407f9dbbf4f0cfd3789723c3; 3677f5555f4b60002c71fb951b5a6e07d0572d08; b608f7987eab03d12ba10a9f53f21c6921ecef09).
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for microsoft/onnxruntime: Feature-focused delivery on QNN Execution Provider enhancements that broaden compatibility and runtime flexibility. Key features delivered include negative padding support aligned with ONNX, an inverse operation for 2x2 matrices, and enabling the TopK 'largest' attribute without requiring pre-sorted output. These changes were implemented across three commits (hashes: 9d650a4b2348d737407f9dbbf4f0cfd3789723c3; 3677f5555f4b60002c71fb951b5a6e07d0572d08; b608f7987eab03d12ba10a9f53f21c6921ecef09).
September 2025
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 performance-focused update: Expanded QNN Execution Provider capabilities in microsoft/onnxruntime, delivering core operation enhancements that improve throughput and model compatibility for attention-based workloads. Implementations include 2D bias fusion in Gemm, Mod operation support (fmod edge case), Thresholded ReLU, and Einsum support for QK attention with capital-letter notation. These changes deliver measurable business value through higher inference performance and wider model support.
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 performance-focused update: Expanded QNN Execution Provider capabilities in microsoft/onnxruntime, delivering core operation enhancements that improve throughput and model compatibility for attention-based workloads. Implementations include 2D bias fusion in Gemm, Mod operation support (fmod edge case), Thresholded ReLU, and Einsum support for QK attention with capital-letter notation. These changes deliver measurable business value through higher inference performance and wider model support.
Activity
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Quality Metrics
Correctness85.6%
Maintainability80.0%
Architecture85.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm OptimizationC++C++ developmentDeep LearningMachine LearningNeural NetworksQNNSoftware TestingTensor Manipulationalgorithm designalgorithm optimizationdeep learningmachine learningunit testing
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