The heavy burdens of computation and off-chip traffic impede deploying the
large scale convolution neural network on embedded platforms. As CNN is
attributed to the strong endurance to computation errors, employing block
floating point (BFP) arithmetics in CNN accelerators could save the hardware
cost and data traffics efficiently, while maintaining the classification
accuracy. In this paper, we verify the effects of word width definitions in BFP
to the CNN performance without retraining. Several typical CNN models,
including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper.
Experiments revealed that 8-bit mantissa, including sign bit, in BFP
representation merely induced less than 0.3% accuracy loss. In addition, we
investigate the computational errors in theory and develop the noise-to-signal
ratio (NSR) upper bound, which provides the promising guidance for BFP based
CNN engine design.

本文在不重新训练的情况下测试了几种经典的卷积神经网络 (CNN) 模型，验证了使用块浮点算法 (BFP) 在 CNN 加速器中定义字宽的效果，并探究了理论计算误差，提出了噪声信号比（NSR）的上限，为基于 BFP 的 CNN 引擎设计提供了有价值的指导。