关键词: 现场可编程门阵列（FPGA）, 逆向传播（BP）神经网络, 手写数字, 逆向传播（BP）算法, Verilog语言

Abstract:

The handwritten digital BP（Back Propagation） neural network consists of an input layer, a hidden layer, and an output layer. It’s training data is 60,000 samples in the MNIST data set, the BP algorithm consists of a stochastic gradient descent algorithm and back propagation algorithm. The network learning process uses 30 small iterations of small batch data to train appropriate network weights and offsets. It is important to design BP algorithm, timing control layer network state, Sigmoid （S-type） function and its derivative linear fitting in Verilog language by using FPGA（Field Programmable Gate Array） hardware platform. The network weight and offset of Gaussian distribution are initialized with a mean of 0 and a variance of 1, and the input samples and labels of the system are simulated and analyzed in Quartus 13.0 and modelsim. In system, small batch data [m] is 10, the learning coefficient [η] is 3, running iteration 30 times is 4.5?s, and recognition correct rate is 91.6%, which satisfies the real-time requirement and has high accuracy.

Key words: Field Programmable Gate Array（FPGA）, Back Propagation（BP） neural network, handwritten digit, Back Propagation（BP） algorithm, Verilog language