Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey

Tmamna, Jihene; Ayed, Emna Ben; Fourati, Rahma; Gogate, Mandar; Arslan, Tughrul; Hussain, Amir; Ayed, Mounir Ben

Abstract

Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced state-of-the-art performance across various disciplines. Yet, the computational demands of these models have escalated exponentially. Intensive computations hinder not only research inclusiveness and deployment on resource-constrained devices, such as Edge Internet of Things (IoT) devices, but also result in a substantial carbon footprint. Green deep learning has emerged as a research field that emphasizes energy consumption and carbon emissions during model training and inference, aiming to innovate with light and energy-efficient neural networks. Various techniques are available to achieve this goal. Studies show that conventional deep models often contain redundant parameters that do not alter outcomes significantly, underpinning the theoretical basis for model pruning. Consequently, this timely review paper seeks to systematically summarize recent breakthroughs in CNN pruning methods, offering necessary background knowledge for researchers in this interdisciplinary domain. Secondly, we spotlight the challenges of current model pruning methods to inform future avenues of research. Additionally, the survey highlights the pressing need for the development of innovative metrics to effectively balance diverse pruning objectives. Lastly, it investigates pruning techniques oriented towards sophisticated deep learning models, including hybrid feedforward CNNs and long short-term memory (LSTM) recurrent neural networks, a field ripe for exploration within green deep learning research.

Citation

Tmamna, J., Ayed, E. B., Fourati, R., Gogate, M., Arslan, T., Hussain, A., & Ayed, M. B. (2024). Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey. Cognitive Computation, 16, 2931–2952. https://doi.org/10.1007/s12559-024-10313-0

• Journal Article Type: Article
• Acceptance Date: May 21, 2024
• Online Publication Date: Jul 5, 2024
• Publication Date: 2024
• Deposit Date: Aug 21, 2024
• Publicly Available Date: Jul 6, 2025
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Pages: 2931–2952
• DOI: https://doi.org/10.1007/s12559-024-10313-0
• Public URL: http://researchrepository.napier.ac.uk/Output/3781474