An Analog Neuron Circuit for Spiking Convolutional Neural Networks Based on Flash Array

In this paper, an Integrate-and-Fire (IF) analog readout neuron circuit is proposed for Spiking Convolutional Neural Network (SCNN) based on flash array. The circuit realizes the following functions: bit line voltage clamping, current readout, current subtraction, and integrate-and-fire. A current readout method is proposed to improve the current readout range and speed by increasing by-pass current. To avoid the loss of array information caused by the traditional analog neuron reset scheme, a reset scheme with subtracting threshold voltage is proposed, which improves the integrity of information and the accuracy of the neural network. The circuit is implemented in 55 nm Complementary Metal Oxide Semiconductor (CMOS) process. Simulation results show that when output current is 20 mu A and 0 mu A, the read speed can be accelerated 100% and 263.6% respectively; The neuron circuit works well. And test results show that, in the current output range of 0 similar to 20 mu A, the clamp voltage error is less than 0.2 mV and the fluctuation is less than 0.4 mV; The linearity of current subtraction can reach 99.9%. To study the performance of the analog neuron circuit, LeNet and AlexNet algorithm with circuit model for the recognition of the MNIST and CIFAR-10 database is tested. Test results illustrate that the neural network accuracy is improved by 1.4% and 38.8%.