Wrap-up

Summary: SME2 matmul microkernel understanding

You’ve successfully navigated one of the more complex areas of AI inference optimization - understanding how low-level SME2 instructions accelerate quantized matrix multiplication. You’ve walked through how an SME2-optimized KleidiAI matmul microkernel:

• Converts weights into a kernel-friendly packed RHS layout
• Quantizes and packs activations into a packed LHS layout
• Uses SME2 INT8 MOPA instructions (smopa) plus LUT-based dequantization (luti4) to compute a 1VL×4VL output tile efficiently
• Dequantizes back to FP32 so the result matches the surrounding FP32 computation (within expected quantization error)

You traced the complete dataflow using a concrete GGML Q4_0 example and can now connect high-level AI frameworks to the Arm hardware features that make them fast.

If you completed the optional hands-on checks, you’ve verified where the key SME2 instructions appear in the microkernel source — valuable experience for anyone working with performance-critical code on Arm platforms.

You’re now equipped to apply the same approach to real workloads:

• Use the llama.cpp performance Learning Path to build and profile llama.cpp with KleidiAI on an SME2-capable device
• Use the ONNX Runtime performance Learning Path to see how similar ideas apply in ONNX Runtime