Low-precision representation of deep neural networks (DNNs) is critical for
efficient deployment of deep learning application on embedded platforms,
however, converting the network to low precision degrades its performance.
Crucially, networks that are designed for embedded applications usually suffer
from increased degradation since they have less redundancy. This is most
evident for the ubiquitous MobileNet architecture which requires a costly
quantization-aware training cycle to achieve acceptable performance when
quantized to 8-bits. In this paper, we trace the source of the degradation in
MobileNets to a shift in the mean activation value. This shift is caused by an
inherent bias in the quantization process which builds up across layers,
shifting all network statistics away from the learned distribution. We show
that this phenomenon happens in other architectures as well. We propose a
simple remedy - compensating for the quantization induced shift by adding a
constant to the additive bias term of each channel. We develop two simple
methods for estimating the correction constants - one using iterative
evaluation of the quantized network and one where the constants are set using a
short training phase. Both methods are fast and require only a small amount of
unlabeled data, making them appealing for rapid deployment of neural networks.
Using the above methods we are able to match the performance of training-based
quantization of MobileNets at a fraction of the cost.

本文探讨了移动设备上深度神经网络低精度表示的问题，提出了一个简单的方法通过在通道的参数中添加一个常数来解决量化引起的移位问题，从而实现了对 MobileNet 架构的优化。