Quantization-Aware Pruning Criterion for Industrial Applications

In this study, we propose a simple and effective quantization-aware neural network pruning method to prune and quantize deep neural networks and then run them on lightweight and cost-effective embedded industrial devices. The proposed pruning method provides a suitable pruning strategy for a given bit precision before performing the subsequent quantization. In particular, the dropout rate is set to be bounded below by the maximum number of quantization levels, which enables the regulation of the tradeoff between sparsity and performance loss. The Kullback–Leibler divergence term of the evidence lower bound is also weighted for adjusting the performance loss. From learned dropout rates, efficient bit allocation is provided to maintain randomness even through the quantization step. In supervised and reinforcement learning applications, the proposed quantization-aware pruning method was proven to be successful in obtaining sparse and lightweight neural networks while maintaining their performance even after quantization. In particular, a real inverted pendulum system was used to determine whether the proposed method works well for actual physical control systems.