Xilinx/onnx-mlir
Nov 2025 – Dec 2025
2 Months active
Languages Used
C++MLIR
Technical Skills
C++C++ developmentMLIRMachine LearningTensor OperationsTensor operations
Gerion Entrup
PROFILE
Gerion Entrup
Gerion Entrup contributed to the Xilinx/onnx-mlir repository by developing robust compiler features and optimizations for ONNX model conversion and deployment. He enhanced the ONNX-to-TOSA conversion path in C++ and MLIR, enforcing static input shapes for DequantizeLinear to prevent runtime errors with dynamic tensors and broadening operator compatibility through dynamic-to-static legalization. Gerion also implemented the PushTransposeDownScalePattern optimization for LayerNorm, improving fusion opportunities and integrating shape helpers for safer transformations. Additionally, he strengthened file I/O error handling in the FrontendDialectHelper, ensuring resilience against corrupted data files and improving system robustness for production machine learning workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
13Total
Bugs
1
Commits
13
Features
2
Lines of code
1,022
Activity Months2
Your Network
1467 peopleSame Organization
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7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
December 2025
10 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Xilinx/onnx-mlir focusing on key deliverables, bug fixes, impact, and technical skills demonstrated. The work emphasizes performance-oriented pattern optimizations in ONNX-MLIR, resilience in Frontend I/O handling, and overall system robustness for production deployments.
10 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Xilinx/onnx-mlir focusing on key deliverables, bug fixes, impact, and technical skills demonstrated. The work emphasizes performance-oriented pattern optimizations in ONNX-MLIR, resilience in Frontend I/O handling, and overall system robustness for production deployments.
December 2025
November 2025
3 Commits • 1 Features
Nov 1, 2025November 2025: Strengthened the ONNX-MLIR to TOSA conversion path against dynamic input issues in DequantizeLinear. Implemented static-shape enforcement, added dynamic-input tests, and introduced dynamic-to-static legalization to improve cross-operator compatibility. These changes reduce runtime errors, improve model deployment reliability, and demonstrate advanced MLIR/C++ proficiency in shaping robust operator legalization and test coverage.
November 2025
3 Commits • 1 Features
Nov 1, 2025November 2025: Strengthened the ONNX-MLIR to TOSA conversion path against dynamic input issues in DequantizeLinear. Implemented static-shape enforcement, added dynamic-input tests, and introduced dynamic-to-static legalization to improve cross-operator compatibility. These changes reduce runtime errors, improve model deployment reliability, and demonstrate advanced MLIR/C++ proficiency in shaping robust operator legalization and test coverage.
Activity
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Quality Metrics
Correctness90.8%
Maintainability81.6%
Architecture84.6%
Performance81.6%
AI Usage24.6%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++C++ developmentCompiler DesignMLIRMachine LearningONNXTensor OperationsTensor operationscompiler designcompiler optimizationdata processingerror handlingfile I/Omachine learningtesting
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