A reconfigurable interconnected filter for face recognition based on convolution neural network

A dynamically reconfigurable hardware model for convolutional neural network (CNN) is presented. The modular prototyping system is based on XILINX FPGAs and is capable of emulating hardware implementations of CNN for the task of face recognition. The system is capable of emulating the complex structure of CNN with exploitation of a small chip area by using the property of reconfiguration. A speedup of about 88 is achieved with FPGA modules of 50 MHz compared to a software implementation on a state of the art personal computer for typical applications of CNN.