Xilinx/onnx-mlir
Mar 2026 – Apr 2026
2 Months active
Languages Used
C++MLIR
Technical Skills
C++C++ developmentcompiler designmachine learningC++ DevelopmentMLIR
Tarik Rosin
PROFILE
Tarik Rosin
Worked on the Xilinx/onnx-mlir repository to deliver a series of targeted performance optimizations and robustness improvements for ONNX model inference. Focused on enhancing computation graph efficiency by removing redundant resize operations, canonicalizing LeakyRelu to Relu where applicable, and optimizing Softmax handling for various axis configurations. Leveraged C++ and MLIR to implement canonicalization patterns, constant folding, and safer transformation logic, including explicit type checks and quantization-safety measures. Expanded test coverage and introduced recomposition patterns for operations like HardSigmoid and ReduceL2, improving maintainability and throughput. The work emphasized software optimization, compiler design, and reliable tensor operation transformations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
7
Lines of code
1,272
Activity Months2
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1659 peopleSame Organization
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Work History
April 2026
12 Commits • 5 Features
Apr 1, 2026April 2026 performance summary for the Xilinx/onnx-mlir project. Delivered targeted ONNX-MLIR optimizations and robustness improvements that enhance inference performance, reduce redundant computation, and improve maintainability on Xilinx platforms. Key contributions focused on Softmax optimization, canonicalization, and safer transformation patterns across the ONNX pipeline, with expanded test coverage and quantization-safety checks to mitigate risk. These changes improve model throughput and memory efficiency for common ONNX graphs, align with ONNX specs, and simplify downstream integration while strengthening overall build quality.
12 Commits • 5 Features
Apr 1, 2026April 2026 performance summary for the Xilinx/onnx-mlir project. Delivered targeted ONNX-MLIR optimizations and robustness improvements that enhance inference performance, reduce redundant computation, and improve maintainability on Xilinx platforms. Key contributions focused on Softmax optimization, canonicalization, and safer transformation patterns across the ONNX pipeline, with expanded test coverage and quantization-safety checks to mitigate risk. These changes improve model throughput and memory efficiency for common ONNX graphs, align with ONNX specs, and simplify downstream integration while strengthening overall build quality.
April 2026
March 2026
2 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Xilinx/onnx-mlir delivered focused performance optimizations through ONNX graph transforms and canonicalization patterns. No major bug fixes were documented for this period in the provided data.
March 2026
2 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Xilinx/onnx-mlir delivered focused performance optimizations through ONNX graph transforms and canonicalization patterns. No major bug fixes were documented for this period in the provided data.
Activity
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Quality Metrics
Correctness97.0%
Maintainability85.6%
Architecture94.4%
Performance90.0%
AI Usage21.6%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++C++ DevelopmentC++ developmentMLIRONNXSoftware optimizationTensor OperationsTensor operationscompiler designmachine learningoptimizationtensor operationstesting
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