Torque based Structured Pruning for Deep Neural Network

Structured pruning is a popular way of convolutional neural network (CNN) acceleration. However, current state of the art pruning techniques require modifications to the network architecture, implementation of complex gradient update rules or repetitive training and long fine-tuning stages. Our novel physics-inspired approach for structured pruning aims to solve these issues. Analogous to ‘Torque’ we apply a force that consolidates the weights of a convolutional layer around a selected pivot point during training. Using the distance-dependency nature of torque, we can encourage high density of weights in filters around this point and increase filter sparsity as we move away. Filters away from the pivot point can be pruned, resulting in a minimum loss of information. We can control the tightness of the weights by varying the hyper-parameters, thus assisting us in creating a more compact network. Our proposed technique is jointly able to perform both filter learning and filter importance sorting. Additionally, our method is easy to implement, requires no change to model architecture and needs very little to no fine-tuning. We show that our approach reaches competitive results with previous state-of-the-art by evaluating popular networks such as VGGNet and ResNet on multiple image classification tasks. Notably, our method can reduce the parameter count of VGGNet by 96% and still maintain the accuracy achieved by the full-size model without any fine-tuning. This makes our method both latency and memory efficient for hardware deployment.