Two Dimensional Convolutions are widely used in Deep Neural Networks.There are several techniques available to perform this operation. They can be implemented using methods such as sliding window, matrix multiplication, vector multiplication etc. In this paper we introduce a new and improved 2-D convolution method called Single Partial Product 2-D Convolution (SPP2D Convolution) that will help calculate 2-D convolution in a fast and expedient manner. We demonstrate that the new SPP2D convolution will prevent recalculation of partial weights and we present theoretical analysis of our technique compared to some other popular techniques.