Xilinx/onnx-mlir
Jan 2026 – Apr 2026
4 Months active
Languages Used
C++MLIR
Technical Skills
C++Compiler DesignMLIRMLIR developmentONNXPerformance Optimization
anup kumar singh
PROFILE
Anup Kumar Singh
Worked on the Xilinx/onnx-mlir repository to deliver a series of compiler and optimization enhancements for ONNX model processing. Focused on improving transformation passes, depthwise convolution optimizations, and robust handling of tensor operations, the work included consolidating and refactoring code in C++ and MLIR to boost performance and maintainability. Implemented new transformation templates, expanded test coverage, and introduced feature flags for controlled deployment of optimizations, particularly in quantization workflows. Addressed stability by rolling back problematic changes and enabling opt-out mechanisms for specialized use cases, while maintaining clear documentation to support collaboration and safe experimentation across machine learning model operators.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
30Total
Bugs
1
Commits
30
Features
7
Lines of code
6,717
Activity Months4
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Work History
April 2026
3 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for Xilinx/onnx-mlir focused on stabilizing ONNX Gather optimization paths while enabling flexible experimentation for quantization workflows. Key changes were implemented in the Gather to Slice/Reshape area, with emphasis on rollback safety, controlled deployment, and clear paths for downstream users to opt out of aggressive optimizations.
3 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for Xilinx/onnx-mlir focused on stabilizing ONNX Gather optimization paths while enabling flexible experimentation for quantization workflows. Key changes were implemented in the Gather to Slice/Reshape area, with emphasis on rollback safety, controlled deployment, and clear paths for downstream users to opt out of aggressive optimizations.
April 2026
March 2026
7 Commits • 1 Features
Mar 1, 2026February 2026-03 monthly summary for Xilinx/onnx-mlir focused on MatMul to XFEConv conversion pass improvements. Key work consolidated to harden the conversion path, boost performance and hardware compatibility, and improve maintainability through refactoring, enhanced tests, and robustness checks. Notable fixes include clang-related adjustments, improved parameter handling, and updated test infrastructure. Also delivered a crash fix by rejecting batched MatMul early to prevent ConvertMatMulToXFEConvPass crashes.
March 2026
7 Commits • 1 Features
Mar 1, 2026February 2026-03 monthly summary for Xilinx/onnx-mlir focused on MatMul to XFEConv conversion pass improvements. Key work consolidated to harden the conversion path, boost performance and hardware compatibility, and improve maintainability through refactoring, enhanced tests, and robustness checks. Notable fixes include clang-related adjustments, improved parameter handling, and updated test infrastructure. Also delivered a crash fix by rejecting batched MatMul early to prevent ConvertMatMulToXFEConvPass crashes.
February 2026
12 Commits • 3 Features
Feb 1, 2026February 2026 (2026-02) monthly wrap-up for Xilinx/onnx-mlir. Delivered core optimization and robustness enhancements to ONNX-MLIR, focusing on depthwise convolution and scale transformations, 4D shape handling, and channel-last resize support. Implemented XFEConv integration and listener enhancements, removed Clip from the processing pipeline, and performed extensive code maintenance to improve stability and readability. These efforts collectively improve model performance, conversion reliability, and maintainability across depthwise/scale-heavy workloads and diverse data layouts.
12 Commits • 3 Features
Feb 1, 2026February 2026 (2026-02) monthly wrap-up for Xilinx/onnx-mlir. Delivered core optimization and robustness enhancements to ONNX-MLIR, focusing on depthwise convolution and scale transformations, 4D shape handling, and channel-last resize support. Implemented XFEConv integration and listener enhancements, removed Clip from the processing pipeline, and performed extensive code maintenance to improve stability and readability. These efforts collectively improve model performance, conversion reliability, and maintainability across depthwise/scale-heavy workloads and diverse data layouts.
February 2026
January 2026
8 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for Xilinx/onnx-mlir: Consolidated optimization passes, expanded transformation coverage, improved test suite, and strengthened pass management. Achieved faster compile times and more robust ONNX model optimizations while boosting maintainability and test reliability.
January 2026
8 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for Xilinx/onnx-mlir: Consolidated optimization passes, expanded transformation coverage, improved test suite, and strengthened pass management. Achieved faster compile times and more robust ONNX model optimizations while boosting maintainability and test reliability.
Activity
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Quality Metrics
Correctness92.0%
Maintainability84.6%
Architecture90.0%
Performance86.0%
AI Usage25.4%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
Algorithm optimizationBuild SystemsC++C++ DevelopmentC++ developmentCode FormattingCompiler DesignCompiler designDebuggingMLIRMLIR developmentMachine LearningONNXOptimizationPerformance Optimization
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