A Fast 2-D Convolution Technique for Deep Neural Networks
Anaam Ansari, Tokunbo Ogunfunmi · 2020
Deep neural networks have revolutionized the technology industry as hardware capable of implementing them has become widely available. They have been utilized in various applications such as image and video processing, self driving cars and speech processing. Two Dimensional (2-D) 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. According to our analysis, our technique can reduce the clock cycles related to input reuse by at least 3 times in comparison with the technique adopted in the work done in and about 9 times than the standard sliding window approach.