December 2025 monthly summary for Xilinx/onnx-mlir. Focused on delivering improvements that enhance model fidelity and runtime efficiency in the ONNX-MLIR pipeline. Key features delivered include a MaxPool fusion optimization that merges back-to-back maxpool operations, extended to quantized paths, with robust validation for kernel sizes, padding, and stride compatibility, plus a comprehensive test suite. Major bugs fixed include a Conv2D activation preservation issue in ONNX conversion for non-grouped convolutions, ensuring the activation function remains intact in outputs. Overall impact: These changes reduce runtime latency by fusing redundant pooling operations, improve the correctness and reliability of ONNX-MLIR conversions, and broaden support for quantized models. They also strengthen the testing and validation framework, contributing to a more stable production pipeline. Technologies/skills demonstrated: ONNX-MLIR conversion pipeline, Conv2D activation handling, MaxPool fusion optimization, quantization support, static shape checks, and test automation (lit tests).

November 2025 monthly summary for Xilinx/onnx-mlir focusing on delivering a feature enhancement for grouped convolution and activation handling, with direct impact on downstream optimizations and execution efficiency.

September 2025 monthly summary for Xilinx/onnx-mlir focusing on aligning MLIR ONNX to TOSA padding test coverage with the updated TOSA dialect API. Implemented test updates to define padding values using tosa.const_shape and adjusted expectations so tosa.pad accepts a shape type, ensuring test suite parity with the updated padding semantics. This work strengthens the ONNX-MLIR -> TOSA path and reduces future maintenance due to API changes.

August 2025 milestone for Xilinx/onnx-mlir focused on quantization correctness and robustness. Implemented quantized exponent handling for ONNX Pow canonicalization, enabling correct Mul-based transformation when exponents come from DequantizeLinear, and added tests and clearer dequantization notes. Also fixed scalar enforcement for scale and zero-point in ONNX ops to improve robustness. Delivered targeted tests and enhanced comments to aid maintenance. Result: improved quantization accuracy, reduced edge-case failures, and stronger compatibility with quantized ONNX workflows.