Xilinx/onnx-mlir
Jan 2026 – Feb 2026
2 Months active
Languages Used
C++MLIR
Technical Skills
C++Compiler DesignMLIRMLIR developmentONNXPerformance Optimization
anup kumar singh
PROFILE
Anup Kumar Singh
Anup Kumar Singh contributed to the Xilinx/onnx-mlir repository by developing and optimizing core compiler passes for ONNX model transformation and performance. Over two months, he consolidated and extended optimization passes, introduced new transformation templates for tensor operations, and improved test coverage to ensure correctness. His work included implementing depthwise convolution and scale transformation optimizations, enhancing 4D tensor shape handling, and supporting channel-last data layouts during ONNX conversion. Using C++, MLIR, and compiler design expertise, Anup focused on code maintainability, robustness, and efficiency, addressing complex tensor manipulation challenges and improving the reliability and performance of ONNX-MLIR compilation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
20Total
Bugs
0
Commits
20
Features
5
Lines of code
5,465
Activity Months2
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Shared Repositories
61
Work History
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
Correctness93.0%
Maintainability86.0%
Architecture93.0%
Performance88.0%
AI Usage27.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
Build SystemsC++C++ DevelopmentC++ developmentCode FormattingCompiler DesignDebuggingMLIRMLIR developmentMachine LearningONNXOptimizationPerformance OptimizationTensor ManipulationTensor Operations
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