In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and allows to decrease the number of product operations by at least two orders of magnitude. The proposed architecture targets custom inference accelerators and can be realized on FPGAs or ASICs. Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be converted without retraining into ShiftCNN with less than 1% drop in accuracy when the proposed quantization algorithm is employed. RTL simulations, targeting modern FPGAs, show that power consumption of convolutional layers is reduced by a factor of 4 compared to conventional 8-bit fixed-point architectures.

介绍了ShiftCNN，这是一种基于2的n次幂权重表示的广义低精度卷积神经网络（CNN）推理架构，可应用于任何具有相对较小权重代码本的CNN架构，并且减少至少两个数量级的乘积运算，可实现的加速器有FPGAs或ASICs。使用提议的量化算法，ImageNet的广泛评估表明可以将最先进的CNN转换为ShiftCNN并且不到1%的准确率下降。

ShiftCNN: 普遍的低精度卷积神经网络推理架构