In this work, we aims to effectively train convolutional neural networks with both low-bitwidth weights and low-bitwidth activations. Optimization of a lowprecision network is typically extremely unstable and it is easily trapped in a bad local minima, which results in noticeable accuracy loss. To mitigate this problem, we propose two novel approaches. On one hand, unlike previous methods that quantize weights and activations simultaneously, we instead propose to use an optimization strategy to progressively finding good local minima. On the other hand, we distill the knowledge from the full-precision model to improve the training process and the final performance of the low-precision model. Furthermore, we can learn our low-precision model from scratch or fine-tune from the full-precision model without losing flexibility. Extensive experiments on various datasets( i.e., CIFAR-100 and ImageNet) show that the proposed method shows minimum loss in accuracy ( i.e., at 2-bit and 4-bit quantization) and sometimes even improves the performance compared to its full-precision counterpart on popular network architectures ( i.e., AlexNet and ResNet-50).

本研究提出了三种简单有效的方法来优化低精度权重和低比特位激活函数的深度卷积神经网络：第一，采用两阶段优化策略以逐步找到好的局部最小值；第二，逐步降低比特数；第三，同时训练全精度模型和低精度模型以提供指引。该方法在不降低网络性能的情况下成功地减小了深度学习训练的计算量和存储需求。

构建有效的低比特宽度卷积神经网络