Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
This work addresses the time-consuming training problem for RL-based auto-pruning, which is important for deploying efficient models in power-constrained scenarios like embedded systems, but it is incremental.
The paper tackles the slow training process of reinforcement learning-based auto-pruning for neural network compression by leveraging historical data, achieving speedups of 1.5-2.5 times for ResNet20 and up to 2.375 times for other networks.
Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1.