Iterative convolutional neural network (ICNN): an iterative CNN solution for low power and real-time systems
Katayoun Neshatpour, Houman Homayoun, Avesta Sasan · 2020
With convolutional neural networks (CNN) becoming more of a commodity in the computer vision field, many have attempted to improve CNN in a bid to achieve better accuracy to a point that CNN accuracies have surpassed that of human's capabilities. However, with deeper networks, the number of computations and consequently the energy needed per classification has grown considerably. In this chapter, an iterative approach is introduced, which transforms the CNN from a single feed-forward network that processes a large image into a sequence of smaller networks, each processing a subsample of each image. Each smaller network combines the features extracted from all the earlier networks, to produce classification results. Such a multistage approach allows the CNN function to be dynamically approximated by creating the possibility of early termination and performing the classification with far fewer operations compared to a conventional CNN.