Pruning is the task of determining importance of components of Artificial Neural Networks (ANN) models and pruning the unimportant parts. Pruning methods include weight pruning, channel(synapse) pruning, and neuron pruning.


  • Smaller model
  • Faster model
  • Reduce computational resource requirements.
  • Reduce computational resources required for training.


  • Avoiding Over-pruning
  • Balancing compression and accuracy


  • Weight Pruning
    • Magnitude pruning: pruning based on near zero weights.
    • Iterative Pruning: pruning over several epochs
  • Channel(synapse) Pruning
  • Neuron Pruning
  • Layer Pruning
  • Filter Pruning
  • Feature map pruning in Convolutional Neural Networks (CNN)
  • Activation Pruning
  • Redundancy Pruning