Xilinx/onnx-mlir
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++MLIRMarkdownPythonShellTableGenYAML
Technical Skills
C++C++ ProgrammingC++ developmentCode formattingCode quality improvementCompiler Design
Ananya
PROFILE
Ananya
Worked on the Xilinx/onnx-mlir repository to advance quantized inference and fused element-wise optimizations, focusing on both performance and maintainability. Developed QLinearEltwise and FusedEltwise operations with shape inference and import mappings, enabling quantized and fused element-wise support across ONNX-MLIR. Implemented optimization passes for Transpose, Slice, and QLinear operations to reduce runtime and memory usage by merging duplicates and removing redundancies. Enhanced automation tooling by improving YAML-based custom operation schema loading and generating operation files from YAML schemas. Emphasized code quality through formatting and cleanup scripts, leveraging C++, Python, and MLIR to streamline backend development and extension workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
5
Lines of code
2,998
Activity Months1
Your Network
1625 peopleSame Organization
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7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
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Abdul Lateef AttarMember
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Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
January 2026
19 Commits • 5 Features
Jan 1, 2026Monthly summary for 2026-01: The ONNX-MLIR efforts progressed significantly in quantized inference support, fused element-wise optimizations, and build tooling, delivering measurable business value in performance, reliability, and maintainability. Key work spanned four areas: quantized ops, fused element-wise patterns, optimization passes, and automation tooling, with additional emphasis on code quality and maintainability.
19 Commits • 5 Features
Jan 1, 2026Monthly summary for 2026-01: The ONNX-MLIR efforts progressed significantly in quantized inference support, fused element-wise optimizations, and build tooling, delivering measurable business value in performance, reliability, and maintainability. Key work spanned four areas: quantized ops, fused element-wise patterns, optimization passes, and automation tooling, with additional emphasis on code quality and maintainability.
January 2026
Activity
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Quality Metrics
Correctness95.8%
Maintainability87.4%
Architecture92.6%
Performance89.4%
AI Usage24.2%
Skills & Technologies
Programming Languages
C++MLIRMarkdownPythonShellTableGenYAML
Technical Skills
C++C++ ProgrammingC++ developmentCode formattingCode quality improvementCompiler DesignCompiler DevelopmentCustom OperationsMLIRMLIR DevelopmentONNXONNX operationsOptimization TechniquesPythonPython development
Repositories Contributed To
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