The integration of discrete AI accelerators with edge processors is poised to revolutionize the capabilities of edge computing, enabling real-time, low-latency and energy-efficient AI applications. As the computational power required for complex AI workloads increases, edge computing applications processors can benefit from discrete AI accelerators. AI accelerators—specialized hardware designed to accelerate machine learning tasks—offload intensive computations, significantly improving processing speed, efficiency and energy consumption. In this presentation, we explore the role of discrete AI accelerators in extending the capabilities of edge processors, discussing their ability to enhance real-time processing, reduce latency and enable autonomous decision-making at the edge. We’ll also examine the integration of AI accelerators with existing edge architectures, the challenges of implementation and considerations such as power management, cost and security.