Lightweight Design and Optimization methods for DCNNs: Progress and Futures
This is a review paper that synthesizes existing research on making DCNNs more efficient for mobile and embedded applications.
This paper reviews lightweight design strategies for Deep Convolutional Neural Networks (DCNNs) to address high computational costs that limit deployment on resource-constrained hardware like mobile devices, examining progress in architectural design and model compression while discussing limitations and future directions.
Lightweight design, as a key approach to mitigate disparity between computational requirements of deep learning models and hardware performance, plays a pivotal role in advancing application of deep learning technologies on mobile and embedded devices, alongside rapid development of smart home, telemedicine, and autonomous driving. With its outstanding feature extracting capabilities, Deep Convolutional Neural Networks (DCNNs) have demonstrated superior performance in computer vision tasks. However, high computational costs and large network architectures severely limit the widespread application of DCNNs on resource-constrained hardware platforms such as smartphones, robots, and IoT devices. This paper reviews lightweight design strategies for DCNNs and examines recent research progress in both lightweight architectural design and model compression. Additionally, this paper discusses current limitations in this field of research and propose prospects for future directions, aiming to provide valuable guidance and reflection for lightweight design philosophy on deep neural networks in the field of computer vision.