In the early days of mobile machine learning, the developer's journey was simple, if a bit primitive. You would bundle a TensorFlow Lite model into your APK, target the CPU, perhaps throw in a bit of NNAPI delegation, and pray that your user's device didn't turn into a pocket heater. It was a monolithic approach: one model, one processor, one execution path.
But the landscape has shifted. We have entered the era of Large Language Models (LLMs) like Gemini Nano and massive diffusion models that demand computational power far beyond the capabilities of a single mobile processor. The "single-accelerator" approach has officially hit a physical wall.
To build the next generation of intelligent, responsive, and battery-efficient mobile applications, we must embrace Heterogeneous Computing. This means moving away from linear execution and toward a sophisticated orchestration model that parallelizes inference across the "Hardware Trinity": the NPU, the GPU, and the DSP.
The Hardware Trinity: Understanding Your Accelerators
To master parallel inference, you first have to understand that your device is not a single brain, but a collection of highly specialized specialists. If you treat them all like a general-purpose CPU, you will fail.
