The widespread application of deep learning has led to the realization of many human-like cognitive tasks in visual analysis. HMAX is a visual cortex-based bio-inspired model that has proven superior to standard computer vision methods in multi-class object recognition. However, due to the high complexity of neural morphology algorithms, implementing HMAX models on edge devices still faces significant challenges. Previous experimental results show that the S2 phase of HMAX is the most time-consuming stage. In this paper, we propose a novel systolic array-based architecture to accelerate the S2 phase of the HAMX model. The simulation results show that compared with the baseline model, the execution time of the most time-consuming S2 phase of