How to Visualize NPU Latency Stages for Better Android AI Performance

Developers optimizing AI models for Android often focus solely on inference time, but real-world latency involves four distinct stages: CPU preparation, memory transfer, NPU computation, and output retrieval. The Neural Processing Unit is a domain-specific chip built for tensor operations, and a key bottleneck is moving data between system RAM and the NPU's local high-speed memory rather than raw compute speed. Google's AICore platform shifts model ownership from individual apps to the Android OS, reducing binary bloat and improving hardware scheduling. Using modern Kotlin and Jetpack Compose, developers can build visualization tools that break down each latency phase to pinpoint performance issues. Identifying whether slowdowns stem from memory bandwidth or compute constraints allows for more targeted and effective optimization.