How Heterogeneous Computing Breaks the Single-Processor Barrier in Mobile AI

Modern mobile AI applications running large language models and diffusion models have outgrown the traditional single-processor approach, pushing developers toward heterogeneous computing on Android. This strategy distributes inference workloads in parallel across three specialized chips: the NPU for energy-efficient tensor operations, the GPU for flexible floating-point computation, and the DSP for real-time sensor data preprocessing. Each processor has distinct strengths and limitations, such as the NPU's rigidity with unsupported operations and the GPU's tendency to cause thermal throttling under heavy loads. A critical bottleneck in this architecture is the Memory Wall, where inefficient data movement between accelerators can negate any performance gains from parallelization. Modern Android systems address this through zero-copy buffers, which share memory handles across processors instead of duplicating data, reducing both latency and energy consumption.