When it comes to Kirin 970 vs Snapdragon 845, the Kirin’s NPU might have an edge but there’s no must-have use case for smartphone machine learning or “AI” yet. Even large percentage points gained or lost in some specific benchmarks isn’t going to make or break the main user experience. All current machine learning tasks can be done on a DSP or even a regular CPU and GPU. An NPU is just a small cog in a much larger system. Dedicated hardware can give an advantage to battery life and performance, but it’s going to be tough for consumers to notice a massive difference given their limited exposure to the applications.
As the machine learning market place evolves and more applications break through, smartphones with dedicated hardware will probably benefit — potentially they’re a bit more future proofed (unless the hardware requirements change). Industry-wide adoption appears to be inevitable, what with MediaTech and Qualcomm both touting machine learning capabilities in lower cost chips, but it’s unlikely the speed of an onboard NPU or DSP is ever going to be the make or break factor in a smartphone purchase.