Compile-Time Memory Layout Optimization for On-Device ML Models

A tutorial outlines a practical Android workflow to cut on-device ML GC pauses without changing the model itself.

It recommends profiling allocation hotspots, adding ML classes to baseline profiles, and reducing ART overhead.

Large tensors should move to direct ByteBuffers, while more work stays below the JNI boundary to avoid extra allocations.

The approach targets fewer GC stalls and frame drops in apps using TFLite, ONNX Runtime, or MediaPipe.