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Logic simulation provides SoC verification with full controllability and observability, but it suffers from very slow simulation speed for complex design. Using hardware emulation such as FPGA can have higher simulation speed. However, it is very hard to debug due to its poor visibility. FPGA-based cosimulation seems to draw a balance, but Design Under Test (OUT) still resides in FPGA and remains hard for debugging. So a run-time RTL debugging methodology for FPGA-assisted verification system is presented. This method provides internal nodes probing on an event-driven cosimulation platform and achieves full observability for OUT. The debugging tools are embedded in HDL simulator using Verilog VPI callback, so signals of testbench and internal nodes of OUT can be observed in a single waveform and updated as simulation runs, making debugging more efficient. The proposed debugging method connects internal nodes directly to a PCI-extended bus, instead of inserting extra scan-chain logic, so the overhead for area is reduced. Our experiment shows that, compared with a similar method in (13], the area overhead for debug logic is reduced by 30-50% and compile time is shortened by 40-70%.

Water level management is an important part of urban water system management. In flood season, the river should be controlled to ensure the ecological and landscape water level. In non-flood season, the water level should be lowered to ensure smooth drainage. In urban areas, the response of the river water level to rainfall and artificial regulation is relatively rapid and strong. Therefore, building a mathematical model to forecast the short-term trend of urban river water levels can provide a scientific basis for decision makers and is of great significance for the management of urban water systems. With a focus on the high uncertainty of urban river water level prediction, a real-time rolling forecast method for the short-term water levels of urban internal rivers and external rivers was constructed, based on long short-term memory (LSTM). Fuzhou City, China was used as the research area, and the forecast performance of LSTM was analyzed. The results confirm the feasibility of LSTM in real-time rolling forecasting of water levels. The absolute errors at different times in each forecast were compared, and the various characteristics and causes of the errors in the forecast process were analyzed. The forecast performance of LSTM under different rolling intervals and different forecast periods was compared, and the recommended values are provided as a reference for the construction of local operational forecast systems.