IQNN: Training Quantized Neural Networks with Iterative Optimizations

Abstract

Quantized Neural Networks (QNNs) use low bitwidth numbers for representing parameters and intermediate results. The lowering of bitwidths saves storage space and allows for exploiting bitwise operations to speed up computations. However, QNNs often have lower prediction accuracies than their floating point counterparts, due to the extra quantization errors. In this paper, we propose a quantization algorithm that iteratively solves for the optimal scaling factor during every forward pass, which significantly reduces quantization errors. Moreover, we propose a novel initialization method for the iterative quantization, which speeds up convergence and further reduces quantization errors. Overall, our method improves prediction accuracies of QNNs at no extra costs for the inference. Experiments confirm the efficacy of our method in the quantization of AlexNet, GoogLeNet and ResNet. In particular, we are able to train a GoogLeNet having 4-bit weights and activations to reach 11.4% in top-5 single-crop error on ImageNet dataset, outperforming state-of-the-art QNNs. The code will be available online.