Structured Pruning of Deep Convolutional Neural Networks

Understand

Real time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses.

• Network pruning is a promising technique to solve this problem.
• However, pruning usually results in irregular network connections that not only demand extra representation efforts but also do not fit well on parallel computation.
• We introduce structured sparsity at various scales for convolutional neural networks, which are channel wise, kernel wise and intra kernel strided sparsity.