microsoft/onnxruntime
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++
Technical Skills
C++machine learningmatrix multiplicationperformance optimizationquantization
Damien Dooley
PROFILE
Damien Dooley
Worked on integrating KleidiAI-optimized microkernels into the microsoft/onnxruntime repository’s MLAS backend, focusing on accelerating SGEMM and IGEMM operations and enabling dynamic quantized matrix multiplication on ARM SMEs (SME2). Developed new packing and dispatch logic to maximize performance on SME2 hardware, and updated the MLAS API to support modular integration of KleidiAI optimizations. The work leveraged C++ and machine learning expertise, with an emphasis on matrix multiplication, performance optimization, and quantization. This engineering effort established a foundation for hardware-aware optimizations, improving inference efficiency for ARM-based deployments and enhancing the extensibility of ONNX Runtime’s low-level computation backend.
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
584 peopleSame Organization
@arm.com538
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
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