Continuous Differential Image-based Fast Convolution for Convolutional Neural Networks

Convolutional neural networks with powerful visual image analysis of deep structures are gaining popularity in many research fields. The main difference in convolutional neural networks compared to other artificial neural networks is the addition of many convolutional layers. The convolutional layer improves the performance of artificial neural networks by extracting feature maps required for image classification. However, for applications that require very low-latency on limited processing resources, the success of a convolutional neural network depends on how fast we can compute. In this paper, we propose a novel convolution technique of fast algorithms for convolutional neural networks using continuous differential images. The proposed method improves the response speed of the algorithm by reducing the computational complexity of the convolutional layer. It is compatible with all types of convolutional neural networks, and the lower the difference in the continuous images, the better the performance. We use the darknet network to benchmark the CPU implementation of our algorithm and show state-of-the-art throughput at pixel difference thresholds from 0 to 25 pixels.