The widespread application of edge AI models is driving chip manufacturers to gradually adopt heterogeneous computing architectures, combining general-purpose processors with semi-custom AI accelerators to meet the demands of specific tasks.In an era where artificial intelligence and edge computing are increasingly integrated, chip manufacturers are facing new challenges, including how to efficiently and low-power process data in different computing scenarios.Compared to cloud computing, edge devices exhibit significant advantages in energy efficiency, real-time performance, and data security, but this also imposes new requirements on chip design.The rise of edge artificial intelligence impacts chip design, including thermal management, architecture optimization, and the implementation of heterogeneous computing, exploring the practical challenges and opportunities brought by technological innovation.
