# Joint Regularization on Activations and Weights for Efficient Neural Network Pruning

## TL;DR

This paper introduces JPnet, a neural network pruning method that jointly regularizes weights and activations, significantly reducing computation while maintaining accuracy.

## Contribution

It proposes a novel joint regularization technique that optimizes both weights and activations for more efficient neural network pruning.

## Key findings

- JPnet achieves up to 98.8% reduction in computation cost.
- Activation and weight numbers are reduced by up to 5.2x and 12.3x.
- Maintains accuracy with only 0.4% degradation.

## Abstract

With the rapid scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency. This work aims to advance the compression beyond the weights to neuron activations. We propose the joint regularization technique which simultaneously regulates the distribution of weights and activations. By distinguishing and leveraging the significance difference among neuron responses and connections during learning, the jointly pruned network, namely \textit{JPnet}, optimizes the sparsity of activations and weights for improving execution efficiency. The derived deep sparsification of JPnet reveals more optimization space for the existing DNN accelerators dedicated for sparse matrix operations. We thoroughly evaluate the effectiveness of joint regularization through various network models with different activation functions and on different datasets. With $0.4\%$ degradation constraint on inference accuracy, a JPnet can save $72.3\% \sim 98.8\%$ of computation cost compared to the original dense models, with up to $5.2\times$ and $12.3\times$ reductions in activation and weight numbers, respectively.