SMTJ-based Dropout Module for In-Memory Computing Bayesian Neural Networks

Bayesian Neural Networks (BayNNs) are powerful tools for making predictions while also providing uncertainty estimates, a crucial aspect in safety-critical systems. However, implementing BayNNs in edge applications poses challenges, often necessitating transformations such as quantification or approximation, especially for Gaussian processes, commonly achieved through Dropout methods. This paper explores a new implementation of the Dropout module by using Superparam-agnetic Tunnel Junction (SMT J). The stochastic circuit enables fine-tuning of the generated stochastic bitstreams used to drop different parameters in the BayNN. The proposed module implementation leads to better power efficiency and robustness against variability. The results demonstrate an improvement in the energy efficiency of up to 34.8× when compared to the related works.