The time step functions as a crucial temporal unit for simulating neuronal dynamics within spiking neural networks, which play a significant role in neuromorphic computing systems. Efficient management of these time steps is vital to ensure model accuracy while optimizing overall system performance. As system scale increases, variations in hardware across subsystems and their asynchronous operations create challenges in achieving effective time step control. To address this issue, this paper proposes an innovative framework for managing time steps in large-scale neuromorphic systems. This framework allows subsystems to dynamically adjust their time step lengths according to computational loads and to perform look-ahead computations. Such a strategy effectively reduces the overhead related to time step synchronization, enhancing system efficiency. Additionally, the paper introduces a safeguard mechanism to ensure the system’s reliability. Experimental results indicate that the proposed framework sustains the correct long-term operation of the system and improves model execution performance by 8.88% to 27.15% when compared to existing methods.