This paper deals with a versatile algorithm for simulating CNN arrays and time multiplexing is implemented using numerical integration algorithm. The approach, time-multiplexing simulation, plays a pivotal role in the area of simulating hardware models and testing hardware implementation of CNN. Owing to hardware limitations in practical sense, it is not possible to have a one-one mapping between the CNN hardware processors and all the pixels of the image. This simulator provides a solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. This article proposes an efficient pseudo code foe exploiting the latency properties of Cellular Neural Network along with well known RK-Fourth Order Embedded numerical integration algorithms. Simulation results and comparison have also been presented to show the efficiency of the Numerical Integration Algorithms. It is found that RK-Embedded Centroidal Mean outperforms well in comparison with the RK-Embedded Harmonic Mean and Embedded Contra-Harmonic Mean.