The recent trend toward increasingly deep convolutional neural networks (CNNs) leads to a higher demand of computational power and memory storage. Consequently, the deployment of CNNs in hardware has become more challenging. In this paper, we propose an Intra-Kernel Regular (IKR) pruning scheme to reduce the size and computational complexity of the CNNs by removing redundant weights at a fine-grained level. Unlike other pruning methods such as Fine-Grained pruning, IKR pruning maintains regular kernel structures that are exploitable in a hardware accelerator. Experimental results demonstrate up to 10x parameter reduction and 7x computational reduction at a cost of less than 1% degradation in accuracy versus the un-pruned case.
@article{arxiv.1803.05909,
title = {Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning},
author = {Maurice Yang and Mahmoud Faraj and Assem Hussein and Vincent Gaudet},
journal= {arXiv preprint arXiv:1803.05909},
year = {2018}
}