| Abstract |
Deep Neural Networks (DNNs) are a powerful tool for solving complex tasks in many application domains. The high performance of DNNs demands significant computational resources, which might not always be available. Network quantization with mixed-precision across the layers can alleviate this high demand. However, determining layer-wise optimal bit-widths is non-trivial, as the search space is exponential. This article proposes a novel technique for allocating layer-wise bit-widths for a DNN using a multi-layer perceptron (MLP). The Kullback-Leibler (KL) divergence of the softmax outputs between the quantized and full precision network is used as the metric to quantify the quantization quality. We explore the relationship between the KL-divergence and the network size, and from our experiments observe that more aggressive quantization leads to higher divergence, and vice versa. The MLP is trained with layer-wise bit-widths as labels and their corresponding KL-divergence as the input. The MLP training set, i.e. the pairs of the layer-wise bit-widths and their corresponding KL-divergence, is collected using a Monte Carlo sampling of the exponential search space. We introduce a penalty term in the loss to ensure that the MLP learns to predict bit-widths resulting in the smallest network size. We show that the layer-wise bit-width predictions from the trained MLP result in reduced network size without degrading accuracy while achieving better or comparable results with SOTA work but with less computational overhead. Our method achieves up to 6x, 4x, 4x compression on VGG16, ResNet50, and GoogLeNet respectively, with no accuracy drop compared to the original full precision pretrained model, on the ImageNet dataset. |