Learning-based Adaptation to Applications and Environments in a Reconfigurable Network-on-Chip

The set of applications communicating via a Network-on-Chip (NoC) and the NoC itself both have varying run-time requirements on reliability and power-efficiency. To meet these requirements, we propose a novel Power-aware and Reliable Encoding Schemes Supported reconfigurable Network-on-Chip (PRESSNoC) architecture which allows processing elements, routers, and data encoding methods to be reconfigured at runtime. Further, an intelligent selection of encoding methods is achieved through a REasoning And Learning (REAL) framework at run-time. An instance of PRESSNoC was implemented on a Xilinx Virtex 4 FPGA device, which required 25.5% lesser number of slices compared to a conventional NoC with a full-fledged encoding method. The average benefit to overhead ratio of the proposed architecture is greater than that of a conventional NoC by 71%, 32%, and 277% when we consider the individual effects of interference rate per instruction, application domains, and system characteristics, respectively. Experiments have thus shown that PRESSNoC induces a higher probability toward the reduction of crosstalk interferences and dynamic power consumption, at the same amount of overheads in performance and hardware usage.