Xilinx/onnx-mlir
Jan 2026 – Feb 2026
2 Months active
Languages Used
C++MLIR
Technical Skills
C++C++ ProgrammingCompiler DesignCompiler DevelopmentDeep LearningDeep Learning Operations
Tuhin Pahari
PROFILE
Tuhin Pahari
Tuhin Pahari contributed to the Xilinx/onnx-mlir repository by developing and optimizing depthwise convolution support for both 2D and 3D NHWC inputs, targeting improved inference efficiency and broader model compatibility. He implemented new compiler passes in C++ and MLIR, enabling quantized and non-quantized tensor operations, and streamlined the conversion of MatMul to Conv for more efficient execution. Tuhin enhanced test automation and code quality by expanding test coverage, addressing linter compliance, and stabilizing model behavior through targeted bug fixes. His work demonstrated a deep understanding of compiler design, quantization, and deep learning operations, resulting in robust, maintainable code improvements.
Overall Statistics
Feature vs Bugs
54%Features
Repository Contributions
25Total
Bugs
6
Commits
25
Features
7
Lines of code
6,835
Activity Months2
Your Network
1467 peopleSame Organization
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Abdul Lateef AttarMember
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Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
February 2026
21 Commits • 6 Features
Feb 1, 2026February 2026 performance summary for Xilinx/onnx-mlir. Delivered feature improvements and critical bug fixes that enhance model compatibility, test coverage, and code quality. Notable work includes DepthwiseConv test improvements with onnx_node_name, MatMul to Conv conversion, reorganization of compiler passes, and XFE op instrumentation. Quantization support and test coverage were strengthened, and overall code quality was improved through lint fixes and stability work.
21 Commits • 6 Features
Feb 1, 2026February 2026 performance summary for Xilinx/onnx-mlir. Delivered feature improvements and critical bug fixes that enhance model compatibility, test coverage, and code quality. Notable work includes DepthwiseConv test improvements with onnx_node_name, MatMul to Conv conversion, reorganization of compiler passes, and XFE op instrumentation. Quantization support and test coverage were strengthened, and overall code quality was improved through lint fixes and stability work.
February 2026
January 2026
4 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on the Xilinx/onnx-mlir workstream. Highlighted work includes Depthwise Convolution enhancements across ONNX, Xcompiler, and XFEConv passes, with a focus on delivering performance and broader model support. The changes target improved inference efficiency for depthwise layers and stronger cross-repo integration, aligning with performance and scalability goals for edge deployment.
January 2026
4 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on the Xilinx/onnx-mlir workstream. Highlighted work includes Depthwise Convolution enhancements across ONNX, Xcompiler, and XFEConv passes, with a focus on delivering performance and broader model support. The changes target improved inference efficiency for depthwise layers and stronger cross-repo integration, aligning with performance and scalability goals for edge deployment.
Activity
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Quality Metrics
Correctness96.8%
Maintainability88.8%
Architecture93.6%
Performance90.4%
AI Usage24.8%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++C++ ProgrammingC++ developmentCode QualityCompiler DesignCompiler DevelopmentCompiler OptimizationCompiler designDeep LearningDeep Learning OperationsLinterMLIRMLIR DevelopmentMLIR optimizationONNX
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