In conventional static implementations for correlated streaming applications, computing resources may be in-efficiently utilized since multiple stream processors may supply their sub-results at asynchronous rates for result correlation or synchronization. To enhance the resource utilization efficiency, we analyze multi-streaming models and implement an adaptive architecture based on FPGA Partial Reconfiguration (PR) technology. The adaptive system can intelligently schedule and manage various processing modules during run-time. Experimental results demonstrate up to 78.2% improvement in throughput-per-unit-area on unbalanced processing of correlated streams, as well as only 0.3% context switching overhead in the overall processing time in the worst-case.