Adaptation of Convolution and Batch Normalization Layer for CNN Implementation on FPGA
Tomyslav Sledević · 2019
The article presents integration process of convolution and batch normalization layer for further implementation on FPGA. The convolution kernel is binarized and merged with batch normalization into a core and implemented on single DSP. The concept is proven on custom binarized convolutional neural network (CNN) that is trained in Matlab to solve object localization task. 16 b precision gives 1.3 % error on the output of joined convolution and batch normalization core. The localization accuracy decreases in average by 7 % from 74 % to 67 %, and it is still tolerable in embedded systems applications.