Design of a high-performance parallel computing platform for spaceborne intelligent programmable PSOC

The "satellite data acquisition and ground processing" mode is difficult to meet the characteristics of detection and recognition in high real-time tasks such as target tracking and disaster detection caused by the large width, high resolution, and massive data of remote sensing images themselves. Propose a master-slave parallel computing architecture based on switching nodes. This architecture uses domestically produced PSOC (Programmable System-On-Chip) intelligent heterogeneous AI(Artificial Intelligence) chips, combined with SRIO(Serial Rapid IO) and Ethernet buses to achieve decoupling between broadcast image data distribution and parallel scheduling of MPI(Message Passing Interface) systems. On the basis of better utilizing the computing performance of single chip processors, it explores parallel computing modes with higher computing power and reliability. This mode enables image detection algorithms with large-scale and complex operator characteristics to be deployed and applied faster and more conveniently in spaceborne environments, which has certain reference significance and research value.