January 2026 (Month: 2026-01) — Focused on delivering quantization improvements in CodeLinaro/onnxruntime. Key feature delivered: Quantized BatchNorm support with per-channel dequantization in the QNN HTP backend. Included unit tests validating correctness. No major bugs fixed this month. Impact: enables more flexible per-channel quantization for BatchNorm in QNN HTP, contributing to smaller model sizes and potentially improved inference accuracy and performance. Technologies demonstrated: per-channel quantization, DQ parameters, QNN HTP backend integration, unit testing, and C++/Backend development.

Concise monthly summary for 2025-12: Focused on QNN Execution Provider improvements in ROCm/onnxruntime, delivering flexibility and correctness for quantized ops and strengthening test coverage to reduce risk in production workloads.

Month 2025-11 – ROCm/onnxruntime: Delivered two quantization-focused changes that improve performance and robustness. QDQ Node Group Detection Performance Enhancement updates the clip min/max retrieval from DequantizeLinear nodes to better identify QDQ groups, enabling more cases to be tagged as valid QDQ node groups and delivering substantial performance boosts for execution providers. ConvTranspose Bias Quantization Axis Fix corrects the expected axis for bias quantization when ConvTranspose has a float bias, with added tests to validate per-channel weights and float bias quantization. Together, these changes strengthen quantization accuracy, reduce false positives in QDQ tagging, and improve deployment readiness across targets. Technologies demonstrated include quantization tooling, DequantizeLinear analysis, per-channel quantization, axis handling, and regression testing.

Monthly work summary for 2025-09 focusing on delivering robustness for quantized GEMM paths in ONNX Runtime and improving test coverage.

August 2025 monthly summary for CodeLinaro/onnxruntime focused on QNN Execution Provider (QNN EP) stability, quantization flexibility, and dynamic weight handling. Delivered robust naming, stability fixes, and transformer-optimized quantization paths that collectively improve inference reliability and performance for production workloads.

July 2025 performance summary for CodeLinaro/onnxruntime (QNN EP): Delivered substantial feature enhancements and stability fixes that broaden ONNX model compatibility and reinforce runtime reliability. Business value achieved includes expanded model support, fewer validation failures, and improved stability across the QNN Execution Provider. Key features delivered include GridSample linear mode support for ONNX opset 20+ and enhanced Einsum support with a new bhwc,hkc->bhwk equation (plus a ReduceSum-Multiply path for broadcast inputs). Major bugs fixed include pool reshape name conflicts during ONNX→QNN conversion, ScatterND rejection narrowed to QNN-CPU, and data type checks updated to skip optional I/Os. Impact: more models can run reliably on QNN EP, reduced maintenance burden, and smoother deployment in production environments. Technologies/skills demonstrated include ONNXRuntime/QNN EP integration, Einsum optimization, model validation, unit tests, and stability hardening across conversion and validation layers.

June 2025 monthly summary for CodeLinaro/onnxruntime focusing on QNN Execution Provider performance enhancements and data-type support. Key work delivered the following features and related improvements, with strong emphasis on business value and technical excellence.

May 2025: Delivered a performance-focused optimization in CodeLinaro/onnxruntime by fusing a scaling operation into the softmax path (Softmax-Scaling Fusion). This optimization reduces the number of operations during inference, improving runtime performance along the QNN EP path. The work was implemented as a commit f9739c2da7e86e8c91058a2b934fe825e03d94b3 with the message "[QNN EP] Fuse scale into softmax (#24809)". Impact includes faster inference throughput and lower latency for models relying on softmax in the evaluation graph, particularly on QNN EP. Skills demonstrated include inference graph optimization, fusion patterns, and cross-team integration with QNN EP workstreams. Roadmap considerations include extending fusion opportunities to adjacent ops and collecting cross-platform benchmarks for ongoing optimization.