Xilinx/onnx-mlir
Nov 2025 – Feb 2026
4 Months active
Languages Used
C++MLIRCMakePython
Technical Skills
C++ developmentC++ programmingMLIRONNXcompiler designtesting
Ilango Rajagopal
PROFILE
Ilango Rajagopal
Contributed to the Xilinx/onnx-mlir repository by developing and refining ONNX quantization and transformation features over four months. Focus areas included enhancing quantization-ready paths, improving location tracking for ONNX Split operations, and strengthening result name propagation for clearer computation graphs. Leveraged C++, MLIR, and CMake to implement robust verification, architectural refactors, and expanded test coverage, addressing both feature development and bug fixes. Emphasized code quality through linter usage, code formatting, and safety improvements, while ensuring reliable model transformations and maintainability. The work enabled more accurate quantized models, safer deployment workflows, and reduced debugging time for ONNX-MLIR users.
Overall Statistics
Feature vs Bugs
58%Features
Repository Contributions
41Total
Bugs
8
Commits
41
Features
11
Lines of code
3,075
Activity Months4
Your Network
70 peopleShared Repositories
70
Abhishek BishtMember
Alexandre EichenbergerMember
Ananya AmancherlaMember
Ananya AmancherlaMember
AnanyaMember
Anil Kumar MarthaMember
anup kumar singhMember
ayanshul-amdMember
Ayush AnshulMember
Work History
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 highlights focused on delivering robust ONNX quantization capabilities, strengthening propagation, and improving maintainability. Key work spanned core quantization improvements, expanded test coverage, and code quality enhancements. Business value includes more accurate, reliable quantized models and reduced risk in production deployments through better testing and safer code.
11 Commits • 3 Features
Feb 1, 2026February 2026 highlights focused on delivering robust ONNX quantization capabilities, strengthening propagation, and improving maintainability. Key work spanned core quantization improvements, expanded test coverage, and code quality enhancements. Business value includes more accurate, reliable quantized models and reduced risk in production deployments through better testing and safer code.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for Xilinx/onnx-mlir: Focused on improving correctness and traceability of ResultNames during ONNX dialect transformations. Key delivered items include a bug fix to initialize new ResultNames to an empty string when replacing operations and a generalized feature to propagate ResultNames for any attribute across ONNX operations. Commits affected: a106b9fac197078322aefa7bf7eb7ff41997cc1b; aceef1ab07dd2209f829fd328b7b1d350081edd3. Impact: increased reliability of result labeling in transformed graphs, reduced debugging time, and smoother model deployment workflows. Technologies/skills demonstrated: MLIR/ONNX-MLIR contribution, C++/dialect code maintenance, robust testing and patching, git-based traceability. Business value: safer model transformations, clearer computation graphs, improved reproducibility and faster iteration for deployment pipelines.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for Xilinx/onnx-mlir: Focused on improving correctness and traceability of ResultNames during ONNX dialect transformations. Key delivered items include a bug fix to initialize new ResultNames to an empty string when replacing operations and a generalized feature to propagate ResultNames for any attribute across ONNX operations. Commits affected: a106b9fac197078322aefa7bf7eb7ff41997cc1b; aceef1ab07dd2209f829fd328b7b1d350081edd3. Impact: increased reliability of result labeling in transformed graphs, reduced debugging time, and smoother model deployment workflows. Technologies/skills demonstrated: MLIR/ONNX-MLIR contribution, C++/dialect code maintenance, robust testing and patching, git-based traceability. Business value: safer model transformations, clearer computation graphs, improved reproducibility and faster iteration for deployment pipelines.
December 2025
25 Commits • 6 Features
Dec 1, 2025December 2025 focused on delivering quantization-ready capabilities in Xilinx/onnx-mlir, with robust verification, improved interoperability with ONNX models, and architectural refinements that reduce maintenance and improve performance potential. The month culminated in a quantization-ready path that adds quantTypes support across passes, better tests, and a cleaner codebase that supports enterprise-scale deployment.
25 Commits • 6 Features
Dec 1, 2025December 2025 focused on delivering quantization-ready capabilities in Xilinx/onnx-mlir, with robust verification, improved interoperability with ONNX models, and architectural refinements that reduce maintenance and improve performance potential. The month culminated in a quantization-ready path that adds quantTypes support across passes, better tests, and a cleaner codebase that supports enterprise-scale deployment.
December 2025
November 2025
3 Commits • 1 Features
Nov 1, 2025In November 2025, delivered key updates to ONNX-MLIR focused on location tracking and node name handling for the ONNX Split operation. Implemented decomposed child location tracking to improve traceability, expanded test coverage to validate location tracking across split scenarios, and corrected casting of ONNX node location attributes to NameLoc to ensure robust transformation patterns. These changes strengthen debuggability, reliability, and maintainability of the ONNX-MLIR transformation passes while reducing risk in production models.
November 2025
3 Commits • 1 Features
Nov 1, 2025In November 2025, delivered key updates to ONNX-MLIR focused on location tracking and node name handling for the ONNX Split operation. Implemented decomposed child location tracking to improve traceability, expanded test coverage to validate location tracking across split scenarios, and corrected casting of ONNX node location attributes to NameLoc to ensure robust transformation patterns. These changes strengthen debuggability, reliability, and maintainability of the ONNX-MLIR transformation passes while reducing risk in production models.
Activity
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Quality Metrics
Correctness92.6%
Maintainability86.8%
Architecture88.8%
Performance86.8%
AI Usage22.0%
Skills & Technologies
Programming Languages
C++CMakeMLIRPython
Technical Skills
C++C++ DevelopmentC++ developmentC++ programmingCMakeCompiler DesignCompiler designMLIRMLIR developmentMachine LearningONNXONNX integrationQuantizationQuantization TechniquesTensor Operations
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