Leveraging Stochasticity for In Situ Learning in Binarized Deep Neural Networks

A recent thrust in deep neural network (DNN) research has been toward binary approaches for compact and energy-sparing neuromorphic architectures utilizing emerging devices. However, approaches to deal with device process variations and the realization of stochastic behavior intrinsically within neural circuits remain underexplored. Herein, we leverage a novel probabilistic spintronic device for low-energy recognition operations that improves DNN performance through active in situ learning via the mitigation of device reliability challenges.