Next-generation AI and computer vision applications for autonomous vehicles, cameras, drones and robots require higher-than-ever computing power. Often, the most efficient way to deliver high performance (especially in cost- and power-constrained applications) is to use multi-core processors. But developers must then map their applications onto the multiple cores in an efficient manner, which can be difficult. To address this challenge and streamline application development, CEVA has introduced the Architecture Planner tool as a new element in CEVA’s comprehensive AI SDK. In this talk, we’ll show how the Architecture Planner tool analyzes the network model and the processor configuration (number of cores, memory sizes), then automatically maps the workload onto the multiple cores in an efficient manner. We’ll explain key techniques used by the tool, including symmetrical and asymmetrical multi-processing, partition by sub-graphs, batch partitioning and pipeline partitioning.