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。