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Related Experiment Video

Updated: Oct 1, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

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DeepCompNet: A Novel Neural Net Model Compression Architecture.

M Mary Shanthi Rani1, P Chitra1, S Lakshmanan1

  • 1Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Dindigul, Tamil Nadu, India.

Computational Intelligence and Neuroscience
|March 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces DeepCompNet, a hybrid pipeline for compressing deep learning models. It achieves a 26x compression ratio on neural networks, enabling efficient deployment on memory-constrained devices.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning models offer significant advancements across various sectors.
  • Large model sizes hinder deployment on resource-constrained devices.

Purpose of the Study:

  • To develop an innovative hybrid compression pipeline for neural networks.
  • To enable efficient deployment of deep learning applications on memory-limited hardware.

Main Methods:

  • A hybrid compression pipeline integrating z-score weight pruning, DBSCAN clustering-based quantization, and Huffman encoding.
  • Experimentation with LeNet Deep Neural Network architectures on MNIST and CIFAR datasets.

Main Results:

  • Achieved a 26x compression ratio for neural networks.
  • Maintained model accuracy post-compression.
  • Demonstrated effective compression using the DeepCompNet model.

Conclusions:

  • The proposed hybrid compression pipeline effectively reduces neural network size.
  • DeepCompNet facilitates the deployment of deep learning on memory-constrained devices.
  • The synergistic approach ensures efficient and accurate model compression.