Demonstration of Generative Adversarial Network by Intrinsic Random Noises of Analog RRAM Devices

For the first time, Generative Adversarial Network (GAN) is experimentally demonstrated on 1kb analog RRAM array. After online training, the network can generate different patterns of digital numbers. The intrinsic random noises of analog RRAM device are utilized as the input of the neural network to improve the diversity of the generated numbers. The impacts of read and write noises on the performance of GAN are analyzed. Optimized methodology is developed to mitigate the excessive noise effect on RRAM based GAN. This work proves that RRAM is suitable for the application of GAN. It also paves a new way to take advantage of the non-ideal effects of RRAM devices.