Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning

Autor:	Yang, Maurice, Faraj, Mahmoud, Hussein, Assem, Gaudet, Vincent
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 8 figures, ISMVL 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1803.05909 Zobrazit plný text záznamu View this record from Arxiv