Xilinx/onnx-mlir
Jan 2026 – May 2026
5 Months active
Languages Used
C++MLIRPythonYAML
Technical Skills
C++C++ ProgrammingCompiler DesignCompiler DevelopmentDeep LearningDeep Learning Operations
Tuhin Pahari
PROFILE
Tuhin Pahari
Over five months, contributed to the Xilinx/onnx-mlir repository by developing and optimizing compiler features for deep learning model deployment. Focused on enhancing depthwise convolution support, quantization reliability, and activation fusion, the work involved implementing new ONNX operations, modernizing activation handling, and improving test automation. Leveraged C++, MLIR, and Python scripting to deliver efficient tensor operations, robust quantization-aware fusions, and automated code generation from YAML. Addressed code quality through linter compliance, refactoring, and CI/CD integration, while resolving bugs and aligning with upstream changes. The engineering approach emphasized maintainability, runtime efficiency, and broader model compatibility for edge inference scenarios.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
75Total
Bugs
20
Commits
75
Features
20
Lines of code
1,058,314
Activity Months5
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1659 peopleSame Organization
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Work History
May 2026
2 Commits
May 1, 2026Performance-focused monthly summary for 2026-05 highlighting features shipped and bugs resolved within the Xilinx/onnx-mlir repo, emphasizing business value and technical impact.
2 Commits
May 1, 2026Performance-focused monthly summary for 2026-05 highlighting features shipped and bugs resolved within the Xilinx/onnx-mlir repo, emphasizing business value and technical impact.
May 2026
April 2026
36 Commits • 10 Features
Apr 1, 2026April 2026 monthly highlights for Xilinx/onnx-mlir: Focused on boosting runtime efficiency, strengthening quantization reliability, and improving code quality. Key features delivered include Relu6 fusion with Conv to improve inference efficiency; per-axis quantization support with tests for Transpose and Transpose-of-Const and dtype handling improvements; the Tile to Add pass with reviewer comment resolutions; XFEGridSample op with 5D input support and conversions; and automation for op definitions and build tables via YAML to XFEOps and a script to generate OpBuildTable.inc. In addition, upstream alignment and targeted bug fixes enhanced stability and maintainability across the project.
April 2026
36 Commits • 10 Features
Apr 1, 2026April 2026 monthly highlights for Xilinx/onnx-mlir: Focused on boosting runtime efficiency, strengthening quantization reliability, and improving code quality. Key features delivered include Relu6 fusion with Conv to improve inference efficiency; per-axis quantization support with tests for Transpose and Transpose-of-Const and dtype handling improvements; the Tile to Add pass with reviewer comment resolutions; XFEGridSample op with 5D input support and conversions; and automation for op definitions and build tables via YAML to XFEOps and a script to generate OpBuildTable.inc. In addition, upstream alignment and targeted bug fixes enhanced stability and maintainability across the project.
March 2026
12 Commits • 3 Features
Mar 1, 2026March 2026: Delivered activation handling modernization and quantization-aware fusion improvements in ONNX-MLIR, with a new activation behavior path and enhanced tests/CI. Result: higher model fidelity, more reliable fused ops, and safer defaults for quantized inference.
12 Commits • 3 Features
Mar 1, 2026March 2026: Delivered activation handling modernization and quantization-aware fusion improvements in ONNX-MLIR, with a new activation behavior path and enhanced tests/CI. Result: higher model fidelity, more reliable fused ops, and safer defaults for quantized inference.
March 2026
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.
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.
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.
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
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Quality Metrics
Correctness94.6%
Maintainability87.2%
Architecture90.6%
Performance88.4%
AI Usage23.2%
Skills & Technologies
Programming Languages
C++MLIRPythonYAML
Technical Skills
Algorithm OptimizationC++C++ DevelopmentC++ ProgrammingC++ developmentCI/CDCode QualityCompiler DesignCompiler DevelopmentCompiler OptimizationCompiler designContinuous IntegrationDeep LearningDeep Learning OperationsDevOps
Repositories Contributed To
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