Xilinx/onnx-mlir
Apr 2025 – May 2025
2 Months active
Languages Used
C++MLIRPython
Technical Skills
Code GenerationCompiler DevelopmentCompiler OptimizationMLIRMLIR Dialect DevelopmentONNX
Arkar-Hema
PROFILE
Arkar-hema
Hema Bhaskar contributed to the Xilinx/onnx-mlir repository by developing and optimizing compiler passes for ONNX model transformations using C++, MLIR, and ONNX Runtime. She implemented a canonicalization fix to ensure MaxPool operations precede Relu when required, improving correctness and processing efficiency. Hema also created an optimization pass to merge nested Concat operations along the same axis, reducing intermediate representation complexity and redundant computation. In addition, she introduced a convolution fusion pass that combines parallel Conv operations sharing the same input, simplifying computation graphs and enabling future performance gains. Her work demonstrated strong depth in compiler optimization and dialect development.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
510
Activity Months2
Your Network
88 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Overview: In May 2025, delivered a new ONNX Convolution Fusion Optimization in the ONNX dialect, introducing a pass to merge parallel Convolution operations that share the same input into a single Conv operation. This reduces redundant computations, simplifies the computation graph, and lays groundwork for further performance optimizations in the ONNX-MLIR integration with Xilinx toolchains. The change is tracked in commit 32ba7210e491cbb83e657375e222b244206e50a1 with message 'Combine Parallel Convolution optimization pass in ONNX Dialect (#3116)'.
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Overview: In May 2025, delivered a new ONNX Convolution Fusion Optimization in the ONNX dialect, introducing a pass to merge parallel Convolution operations that share the same input into a single Conv operation. This reduces redundant computations, simplifies the computation graph, and lays groundwork for further performance optimizations in the ONNX-MLIR integration with Xilinx toolchains. The change is tracked in commit 32ba7210e491cbb83e657375e222b244206e50a1 with message 'Combine Parallel Convolution optimization pass in ONNX Dialect (#3116)'.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Xilinx/onnx-mlir focusing on business value and technical achievements. Delivered two high-impact changes in the ONNX dialect with clear operational benefits: (1) Bug fix in canonicalization to ensure MaxPool precedes Relu when Relu input is not directly from a Conv output, improving correctness and processing efficiency; (2) New optimization pass to merge nested Concat operations along the same axis into a single Concat, reducing redundant computations and IR complexity. These changes streamline model transformations, reduce runtime overhead for compiled ONNX models, and improve maintainability of the ONNX dialect optimization pipeline.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Xilinx/onnx-mlir focusing on business value and technical achievements. Delivered two high-impact changes in the ONNX dialect with clear operational benefits: (1) Bug fix in canonicalization to ensure MaxPool precedes Relu when Relu input is not directly from a Conv output, improving correctness and processing efficiency; (2) New optimization pass to merge nested Concat operations along the same axis into a single Concat, reducing redundant computations and IR complexity. These changes streamline model transformations, reduce runtime overhead for compiled ONNX models, and improve maintainability of the ONNX dialect optimization pipeline.
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture83.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
C++Code GenerationCompiler DevelopmentCompiler OptimizationMLIRMLIR Dialect DevelopmentONNXONNX RuntimeOptimizationOptimization Passes
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