You’ve optimized your model. You’ve pruned the weights, applied quantization, and selected the perfect architecture. But when you deploy your Edge AI feature to a real-world Android device, the experience feels... off. The frame rate stutters, the device heats up, and your "real-time" detection feels more like a slideshow.
If you are only looking at "inference time" as a single metric, you are missing the bigger picture. In the world of high-performance mobile AI, latency isn't a single number; it is a complex, multi-stage dance between the CPU, the NPU, and the system memory. To build truly fluid AI experiences, you need to stop guessing and start visualizing.
In this guide, we will decompose the anatomy of NPU latency, explore the architectural shift brought by Google's AICore, and walk through a professional-grade implementation using modern Kotlin and Jetpack Compose.
The Anatomy of NPU Latency: Moving Beyond Abstraction
To visualize NPU (Neural Processing Unit) latency effectively, we must move beyond the abstraction of "inference time" and decompose the process into its constituent physical and logical stages.
