Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design

Understand

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.