FPGA implementation of back propagation neural network - Li, Jianing (Nanyang Technological University, 2022)
This project presented a backpropagation neural network on FPGA which can conduct inference and training processes for linear and non-linear problems. The network structure chosen contains 3 input nodes, one hidden layer with three neuron units and 1 output node. In addition, this project compares the training time between MATLAB and FPGA. The FPGA can achieve a much shorter training time owing to architecture advantage and computation data type simplification. In the end, the result of the neural network is displayed on the LEDs on the FPGA board. Keywords: