December 2025 — Intel/onnxruntime: Key features delivered and major fixes; focus on stability and quantization reliability. Key features delivered: - Added utility DoesConcatInputShapeContainZero to detect 0-dim tensors in Concat, preventing runtime errors in QNN EP. Commit 8f6c25f714ce8aa0925a463e4937609a3ecb74fc. - Fixed static quantization runner: corrected input file order by enumerating indices; ensured CumSum is registered for quantization, improving data processing and calibration. Commit 8e52f390f7459ea59d79fcd089d23fdac9f33181. Major bugs fixed: - Concat 0-dim tensor runtime error resolved by the new utility. - Quantization runner input order and CumSum quantization registration issues resolved. Overall impact and accomplishments: - Enhanced runtime stability for edge-case inputs and improved calibration reliability of quantized models; reduced production risk. Technologies/skills demonstrated: - C++, Python, QNN EP, QDQ registry, static quantization workflow; focused changes with clear impact on stability and accuracy.

In August 2025, delivered a focused bug fix for the PoolOpBuilder in ONNX Runtime's QNN Execution Provider to correctly handle 5D input shapes. The change revises input-shape checks and pooling call paths to ensure accurate output shape calculation, preventing misinference and assertion failures in Debug builds. This improves model fidelity and stability for 5D tensor pooling across workloads, reducing debugging time for contributors and enhancing reliability in production inference.