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Arm SME2 (Scalable Matrix Extension 2) delivers up to 3.9x speedup for on-device ML inference when running image segmentation models like SqueezeSAM through PyTorch's ExecuTorch runtime. On a single CPU core, INT8 inference improves by 1.83x (556ms to 304ms) and FP16 by 3.9x (1,163ms to 298ms), making FP16 nearly as fast as INT8 in this case. Operator-level profiling reveals that SME2 accelerates matrix operations (convolution and GEMM) by 3-9x, but shifts the bottleneck to data movement operations, particularly transpose operations that account for ~40% of runtime. This acceleration enables interactive mobile AI features like Instagram's cutout tool to run efficiently on-device while preserving CPU headroom for other app functions.

Accelerating On-Device ML Inference with ExecuTorch and Arm SME2 – PyTorch

The Stack: PyTorch, ExecuTorch, XNNPACK, Arm KleidiAI, and SME2

Results: INT8 and FP16 (1 CPU core vs 4 CPU cores)

Three Insights from End-to-End and Operator-Level Results

Hands-On Example: Reproducing the Workflow

Conclusion: What SME2 Changes in Practice