Xilinx/onnx-mlir
Feb 2026 – Feb 2026
1 Month active
Languages Used
C++
Technical Skills
C++C++ programmingcompiler designmachine learningmachine learning model optimization
Ananya Amancherla
PROFILE
Ananya Amancherla
Developed the initial integration of the VitisAI Execution Provider for ONNX Runtime within the microsoft/onnxruntime-genai repository, enabling hardware-accelerated inference on Vitis AI-capable devices. Focused on C++ development and API design, the work introduced configuration options to register a custom operations library, allowing for hardware-optimized inference workflows. The implementation established a foundation for future hardware-tested validation and continuous integration support. By aligning with software architecture best practices, this feature enhanced enterprise performance and reduced latency for generative AI workloads. No major bugs were reported during the period, reflecting a stable and focused approach to feature delivery and integration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
74
Activity Months1
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Shared Repositories
131
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 (Xilinx/onnx-mlir): Delivered targeted improvements to ONNX BatchNorm integration that enhance performance tuning, data-type coverage, and backend compatibility. Implemented an optional BatchNorm decomposition toggle and extended data type support, along with aligning xcompiler changes via a rebase for easier maintenance and upstream parity. These changes improve deployment flexibility on Xilinx backends and strengthen production reliability for BatchNorm workloads.
2 Commits • 1 Features
Feb 1, 2026February 2026 (Xilinx/onnx-mlir): Delivered targeted improvements to ONNX BatchNorm integration that enhance performance tuning, data-type coverage, and backend compatibility. Implemented an optional BatchNorm decomposition toggle and extended data type support, along with aligning xcompiler changes via a rebase for easier maintenance and upstream parity. These changes improve deployment flexibility on Xilinx backends and strengthen production reliability for BatchNorm workloads.
February 2026
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ programmingcompiler designmachine learningmachine learning model optimization
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