1. Why Dedicated Neural Hardware Exists
CPUs remain flexible and GPUs handle wide parallel workloads, but neither is always the most energy-efficient choice for repeated neural-network operations. NPUs add specialized data paths and arithmetic units that can process common model layers while consuming less power.
2. Memory Movement Is Often the Real Bottleneck
A processor cannot use its full arithmetic capacity if model weights and intermediate data arrive too slowly. Shared memory, on-chip caches, compression, and careful scheduling therefore have a direct effect on response time, energy use, and sustained performance.
3. Efficiency Depends on the Whole Stack
New manufacturing processes can improve density and power characteristics, but hardware alone does not guarantee a better experience. Model design, quantization, compilers, thermal management, and application logic must all work together to turn peak capability into reliable everyday features.