microsoft/onnxruntime
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++
Technical Skills
C++machine learningmatrix multiplicationperformance optimizationquantization
Damien Dooley
PROFILE
Damien Dooley
Damien Dooley integrated KleidiAI-optimized microkernels into the microsoft/onnxruntime repository, focusing on accelerating SGEMM and IGEMM operations within the MLAS backend for ARM SMEs (SME2). He implemented new packing and dispatch logic in C++ to maximize matrix multiplication performance and added support for dynamic quantized MatMul, addressing the need for efficient inference on ARM-based hardware. Damien updated the MLAS API to accommodate modular integration of KleidiAI, ensuring future extensibility. His work demonstrated depth in performance optimization, quantization, and low-level machine learning infrastructure, establishing a robust foundation for hardware-aware optimizations in ONNX Runtime’s matrix computation pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,391
Activity Months1
Your Network
505 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for microsoft/onnxruntime: Delivered KleidiAI-optimized microkernels integration into ONNX Runtime's MLAS backend to accelerate SGEMM and IGEMM, and support dynamic quantized MatMul on ARM SMEs (SME2). Implemented new packing and dispatch logic to maximize performance on SME2 and updated the MLAS API to accommodate KleidiAI integration (commit cd450d1563d65fcf8d1748daad894bc036e9efad). This work establishes a foundation for hardware-aware optimizations and improved inference efficiency on ARM-based deployments.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for microsoft/onnxruntime: Delivered KleidiAI-optimized microkernels integration into ONNX Runtime's MLAS backend to accelerate SGEMM and IGEMM, and support dynamic quantized MatMul on ARM SMEs (SME2). Implemented new packing and dispatch logic to maximize performance on SME2 and updated the MLAS API to accommodate KleidiAI integration (commit cd450d1563d65fcf8d1748daad894bc036e9efad). This work establishes a foundation for hardware-aware optimizations and improved inference efficiency on ARM-based deployments.
July 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance100.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++machine learningmatrix multiplicationperformance optimizationquantization
Repositories Contributed To
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