HeadStart: Enforcing Optimal Inceptions in Pruning Deep Neural Networks for Efficient Inference on GPGPUs

Deep convolutional neural networks are well-known for the extensive parameters and computation intensity. Structured pruning is an effective solution to obtain a more compact model for the efficient inference on GPGPUs, without designing specific hardware accelerators. However, previous works resort to certain metrics in channel/filter pruning and count on labor intensive fine-tunings to recover the accuracy loss. The "inception" of the pruned model, as another form factor, has indispensable impact to the final accuracy but its importance is often ignored in these works. In this paper, we prove that optimal inception will be more likely to induce a satisfied performance and shortened fine-tuning iterations. We also propose a reinforcement learning based solution, termed as HeadStart, seeking to learn the best way of pruning aiming at the optimal inception. With the help of the specialized head-start network, it could automatically balance the tradeoff between the final accuracy and the preset speedup rather than tilting to one of them, which makes it differentiated from existing works as well. Experimental results show that HeadStart could attain up to 2.25x inference speedup with only 1.16% accuracy loss tested with large scale images on various GPGPUs, and could be well generalized to various cutting-edge DCNN models.