2026-04 monthly summary focusing on the ONNX-MLIR conversion work and test coverage improvements. Delivered a targeted pattern to convert ONNX ReduceMeanV13 to an AveragePool operation, with axis handling aligned to global average pooling semantics. Added end-to-end test coverage to validate the new pattern and ensure correctness. This work enhances the stability and compatibility of the ONNX lowering path and reduces risk of incorrect pooling semantics in downstream models.

March 2026 monthly summary for Xilinx/onnx-mlir: Delivered key feature enhancements to the reduction-to-convolution path, including axis-based reduction handling, Clang compatibility improvements, quantized bias/weight scaling, and robust test coverage. The work targeted efficient model conversions and improved hardware deployment readiness on Xilinx targets, with a focus on F2-model compatibility and correctness of quantized paths.

February 2026: Xilinx/onnx-mlir — Depthwise Convolution ONNX export improvements delivering robustness, pattern fusion, and performance improvements to enable reliable deployment of depthwise models.

January 2026 (2026-01) – Delivered critical feature and reliability enhancements to Xilinx/onnx-mlir. Key accomplishments include enabling a channel-last (NHWC) resize operation in the ONNX dialect with dynamic shape inference and verification, updating tests and schemas, and ensuring smooth NCHW↔NHWC conversion. Migrated essential compiler passes from flexml to onnx-mlir, including 3D→2D conversion, depthwise convolution, and pooling, and added depthwise convolution with channel multiplier. Also applied code formatting improvements for readability and maintainability. Overall, these changes broaden model compatibility, improve performance potential, and strengthen code quality and test coverage.