Abstract
This paper presents a novel approach to make convolutional neural networks (CNNs) efficient by reducing their computational cost and memory footprint. Even though large-scale CNNs show state-of-the-art performance in many tasks, high computational costs and the requirement of a large memory footprint make them resource-hungry. Therefore, deploying large-scale CNNs on resource-constrained devices poses significant challenges. To address this challenge, we propose to use quaternion CNNs, where quaternion algebra enables the memory footprint to be reduced. Furthermore, we investigate methods to reduce the memory footprint and computational cost further through pruning the quaternion CNNs. Experimental evaluation of the audio tagging task involving the classification of 527 audio events from AudioSet shows that the quaternion algebra and pruning reduce memory footprint by 90% and computational cost by 70% compared to the original CNN model while maintaining similar performance.