The liquid state machine is a kind of spiking neural network that usually is mapped to an NoC-based neuromorphic processor to perform tasks such as classification. The creation of these LSM models does not consider the structure of NoC which resulting in heavy communication pressure on the NoC. In this paper, we propose a hardware aware LSM network generation framework. By keeping the communication between neurons within cores as much as possible, this framework could reduce the communication overheads between cores effectively. The experiment result shows that the LSM model produced by our framework could achieve state-of-art accuracy and is hardware-friendly.