Inference for state-of-the-art deep neural networks is computationally
expensive, making them difficult to deploy on constrained hardware
environments. An efficient way to reduce this complexity is to quantize the
weight parameters and/or activations during training by approximating their
distributions with a limited entry codebook. For very low-precisions, such as
binary or ternary networks with 1-8-bit activations, the information loss from
quantization leads to significant accuracy degradation due to large gradient
mismatches between the forward and backward functions. In this paper, we
introduce a quantization method to reduce this loss by learning a symmetric
codebook for particular weight subgroups. These subgroups are determined based
on their locality in the weight matrix, such that the hardware simplicity of
the low-precision representations is preserved. Empirically, we show that
symmetric quantization can substantially improve accuracy for networks with
extremely low-precision weights and activations. We also demonstrate that this
representation imposes minimal or no hardware implications to more
coarse-grained approaches. Source code is available at
this https URL

本文介绍了一种量化方法 —— 通过学习特定权重子组的对称码本，来减少权重亚群在前向传播和反向传播中的梯度误差，从而提高超低精度权重和激活的网络的准确性，并证明此表示对更粗粒度的方法没有或仅有最小的硬件影响。该方法可用于神经网络量化。