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A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data

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Abstract

Deep learning (DL) models are computationally expensive in space and time, which makes it difficult to deploy DL models in edge computing devices, such as Raspberry-Pi or Jetson Nano. The current strategy uses genetic algorithm (GA), which compresses the deep convolution neural network models without compromising performance. GA was applied by converting the CNN layers into binary vectors. Further, the fitness function in GA was computed based on (i) the minimization of hidden units and (ii) test accuracy. The GA-based strategy was applied on different pre-trained architectures, namely AlexNet, VGG16, SqueezeNet, and ResNet50, respectively, by using three kinds of datasets, namely MNIST, CIFAR-10, and CIFAR-100. The proposed approach demonstrated the reduction in the storage space of AlexNet by 87.62%, 80.97%, and 86.20% corresponding to the datasets MNIST, CIFAR-10, and CIFAR-100, respectively. Further, for the same three datasets, namely VGG16, ResNet50, and SqueezeNet, the system average compression was 91.15%, 78.42%, and 38.40%, respectively. In addition to that, the inference time of the models using proposed strategy was significantly improved with an average of the four datasets of ~ 35.61%, 9.23%, 73.76%, and 79.93% corresponding to AlexNet, SqueezeNet, ResNet50, and VGG16 models. Further, our method when applied to the proposed CNN using the LIDC-IDRI dataset showed a 90.3% reduction in the storage space and inference time. DL system when optimized using GA shows improved performance in both storage and execution time.

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Authors and Affiliations

  1. CSE Department, CMR College of Engineering & Technology, Hyderabad, India

    Sanagala S. Skandha & Vijaya K. Koppula

  2. Department of CSE, Bennett University, Greater Noida, UP, India

    Sanagala S. Skandha, Mohit Agarwal, Kumar Utkarsh & Suneet K. Gupta

  3. Global Biomedical Technologies, Inc., Roseville, CA, USA

    Jasjit S. Suri

  4. Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, 95661, USA

    Jasjit S. Suri

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  1. Sanagala S. Skandha
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  2. Mohit Agarwal
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  6. Jasjit S. Suri
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Correspondence to Suneet K. Gupta or Jasjit S. Suri.

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Skandha, S.S., Agarwal, M., Utkarsh, K. et al. A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data. Neural Comput & Applic 34, 20915–20937 (2022). https://doi.org/10.1007/s00521-022-07567-w

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