Rewarded Meta-Pruning: Meta Learning with Rewards for Channel Pruning

Athul Shibu, Abhishek Kumar, Heechul Jung and Dong-Gyu Lee ()
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Athul Shibu: Department of Artificial Intelligence, Kyungpook National University, Buk-gu, Daegu 41566, Republic of Korea
Abhishek Kumar: Department of Artificial Intelligence, Kyungpook National University, Buk-gu, Daegu 41566, Republic of Korea
Heechul Jung: Department of Artificial Intelligence, Kyungpook National University, Buk-gu, Daegu 41566, Republic of Korea
Dong-Gyu Lee: Department of Artificial Intelligence, Kyungpook National University, Buk-gu, Daegu 41566, Republic of Korea

Mathematics, 2023, vol. 11, issue 23, 1-19

Abstract: Convolutional neural networks (CNNs) have gained recognition for their remarkable performance across various tasks. However, the sheer number of parameters and the computational demands pose challenges, particularly on edge devices with limited processing power. In response to these challenges, this paper presents a novel approach aimed at enhancing the efficiency of deep learning models. Our method introduces the concept of accuracy and efficiency coefficients, offering a fine-grained control mechanism to balance the trade-off between network accuracy and computational efficiency. At our core is the Rewarded Meta-Pruning algorithm, guiding neural network training to generate pruned model weight configurations. The selection of this pruned model is based on approximations of the final model’s parameters, and it is precisely controlled through a reward function. This reward function empowers us to tailor the optimization process, leading to more effective fine-tuning and improved model performance. Extensive experiments and evaluations underscore the superiority of our proposed method when compared to state-of-the-art techniques. We conducted rigorous pruning experiments on well-established architectures such as ResNet-50, MobileNetV1, and MobileNetV2. The results not only validate the efficacy of our approach but also highlight its potential to significantly advance the field of model compression and deployment on resource-constrained edge devices.

Keywords: convolutional neural networks; meta-pruning; ResNet-50; reward function; channel pruning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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