
Worked on the Xilinx/onnx-mlir repository to overhaul ONNX-to-MLIR transformation passes, introducing a new optimization for QDQ Sigmoid operations and refactoring the pass manager for improved maintainability and performance. Enhanced quantization workflows by enabling independent input and output data types in XCOMPILERRequantize, expanding support to int32 and int64, and updating tests to reflect new dimensionality behaviors. Addressed reliability issues in ROI-related transforms and TopK handling, aligning test suites with updated NoValue operand patterns. Leveraged C++, MLIR, and ONNX expertise to streamline compiler design, reduce transformation complexity, and lay groundwork for future optimizations and broader hardware compatibility.
May 2026 (2026-05) highlights focused on stabilizing ROI-related transforms and TopK paths in Xilinx/onnx-mlir, plus aligning the test suite to new NoValue operand patterns. The changes deliver improved reliability for downstream layout conversions, reduce rewrite iteration blockers, and set the foundation for more robust optimizations in subsequent passes.
May 2026 (2026-05) highlights focused on stabilizing ROI-related transforms and TopK paths in Xilinx/onnx-mlir, plus aligning the test suite to new NoValue operand patterns. The changes deliver improved reliability for downstream layout conversions, reduce rewrite iteration blockers, and set the foundation for more robust optimizations in subsequent passes.
April 2026 monthly summary focusing on advancing XCOMPILERRequantize capabilities and test coverage within Xilinx/onnx-mlir. Delivered independent input/output dtypes and expanded supported types to int32 and int64, regenerated op code, and updated tests to reflect dimensionality changes, strengthening the ONNX-MLIR quantization workflow and enabling broader hardware compatibility.
April 2026 monthly summary focusing on advancing XCOMPILERRequantize capabilities and test coverage within Xilinx/onnx-mlir. Delivered independent input/output dtypes and expanded supported types to int32 and int64, regenerated op code, and updated tests to reflect dimensionality changes, strengthening the ONNX-MLIR quantization workflow and enabling broader hardware compatibility.
March 2026: Overhauled ONNX-to-MLIR transformation passes in Xilinx/onnx-mlir, delivering a new optimization pass for replacing QDQ Sigmoid operations, refactoring the pass manager for maintainability and performance, and removing an obsolete passes file to streamline the codebase. This work reduces transformation complexity and accelerates compilation of ONNX models to MLIR backends, creating a solid foundation for future performance improvements.
March 2026: Overhauled ONNX-to-MLIR transformation passes in Xilinx/onnx-mlir, delivering a new optimization pass for replacing QDQ Sigmoid operations, refactoring the pass manager for maintainability and performance, and removing an obsolete passes file to streamline the codebase. This work reduces transformation complexity and accelerates compilation of ONNX models to MLIR backends, creating a solid foundation for future performance improvements.

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