On the Compression of Neural Networks Using $\ell_0$-Norm Regularization and Weight Pruning

Understand

Despite the growing availability of high-capacity computational platforms, implementation complexity still has been a great concern for the real-world deployment of neural networks.

• This concern is not exclusively due to the huge costs of state-of-the-art network architectures, but also due to the recent push towards edge intelligence and the use of neural networks in embedded applications.
• In this context, network compression techniques have been gaining interest due to their ability for reducing deployment costs while keeping inference accuracy at satisfactory levels.
• The present paper is dedicated to the development of a novel compression scheme for neural networks.