  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.
