Bayesian Neural Network Realization by Random Telegraph Noise in 40nm TaO_X ReRAM CiM

Bayesian neural network (BayNN) on ReRAM CiM is realized by exploiting the readout variation of ReRAM devices, such as Random Telegraph Noise (RTN), while conventional BayNNs use Gaussian distributions. Multiple samplings in inference are produced by leveraging the change in each readout current due to RTN. By writing LRS to both sides of a differential pair, the mean and standard deviation of the cells can be programmed to fit the weights of the trained BayNN model. Proposed BayNN with CiM noise achieves performance improvements of 9% and 3% compared with conventional NNs for out-of-distribution detection and adversarial attack detection, respectively.