Smartphones are undergoing a hardware architecture evolution driven by the demand for low-latency, private artificial intelligence features. Silicon vendors are allocating larger die areas to dedicated neural processing units in current generation flagship system-on-chips.
Edge Computing and Battery Efficiency
Executing machine learning inference directly on device eliminates API transmission latency and enhances data security. However, managing thermal dissipation and battery drain during sustained AI workloads remains a central engineering constraint for hardware designers.