March 2026 — Feature delivered: integrated the Arm KleidiAI SME2 BF16 kernel into the MLAS SBGEMM path of intel/onnxruntime, enabling SME-enabled devices to leverage optimized BF16 computations for improved inference performance. The integration, tracked in commit 0be5cc1d16b1717148037b82340682061d6a9fcc, establishes a stable foundation for SME2 optimizations with no API surface changes for users. Business value: higher throughput and better efficiency on ARM SME hardware, expanding ONNX Runtime's ARM ecosystem support.

February 2026 monthly summary for CodeLinaro/onnxruntime. Delivered a feature to improve dynamic kernel selection in the matrix-multiplication path by introducing DynamicQGemm function pointer overrides and a ukernel interface to switch between SME and SME2 variants within KleidiAI. This enables runtime selection of the most suitable microkernel, improving flexibility and potential performance on performance-critical workloads. The work closes issue 26377 and establishes a foundation for ongoing microkernel experimentation and benchmarking.

Month: 2025-09 Concise monthly summary for performance review focused on business value and technical achievements in microsoft/onnxruntime. Key features delivered: - Performance-optimized FP32 kernel integration with SME1/SME2 distinction: Integrated SME1 FP32 kernels into the ONNX Runtime framework and introduced explicit differentiation between SME1 and SME2 kernels to boost FP32 matrix multiplications, facilitating faster inference for performance-sensitive workloads. Major bugs fixed: - No major bugs reported or fixed in this month data set. Overall impact and accomplishments: - Delivered a kernel-level performance enhancement that directly improves throughput for FP32-based inference in ONNX Runtime, benefiting customers using performance-critical models. - Established a foundation for SME1/SME2 aware dispatch, enabling targeted optimizations and easier future tuning. - Documented and tracked changes through a concrete commit tied to the feature (see commits below), supporting maintainability and traceability. Technologies/skills demonstrated: - Low-level kernel integration and optimization (FP32, SGEMM, SME architectures) - Kernel dispatch strategies and performance benchmarking considerations - Code traceability and collaboration with open-source contributions (commit referenced) Commit reference highlights: - ec3bf7f03d9363ebf5c6c952a7f017fc42d7417f: Integrate SME1 SGEMM KleidiAI kernels (#25760) - Represents the core integration work for SME1 FP32 kernels within ONNX Runtime