For computer vision applications, prior works have shown the efficacy of
reducing the numeric precision of model parameters (network weights) in deep
neural networks but also that reducing the precision of activations hurts model
accuracy much more than reducing the precision of model parameters. We study
schemes to train networks from scratch using reduced-precision activations
without hurting the model accuracy. We reduce the precision of activation maps
(along with model parameters) using a novel quantization scheme and increase
the number of filter maps in a layer, and find that this scheme compensates or
surpasses the accuracy of the baseline full-precision network. As a result, one
can significantly reduce the dynamic memory footprint, memory bandwidth,
computational energy and speed up the training and inference process with
appropriate hardware support. We call our scheme WRPN - wide reduced-precision
networks. We report results using our proposed schemes and show that our
results are better than previously reported accuracies on ILSVRC-12 dataset
while being computationally less expensive compared to previously reported
reduced-precision networks.

本研究旨在通过使用一种新的量化方案来缩减激活函数，从而提高计算机视觉应用的性能。我们发现这种方案可以在不降低模型精度的情况下，大大降低动态内存占用、内存带宽、计算能量并加速训练和推理过程。我们称其为宽面减少精度网络，研究表明其结果优于以前提出的精度网络。