Asymmetric Crosspoint Element

Asymmetric crosspoint elements, characterized by non-symmetrical behavior, are being investigated for their application in diverse fields, from deep neural network training to image processing and robotics. Current research focuses on mitigating the negative impacts of this asymmetry through novel training algorithms like Stochastic Hamiltonian Descent, and on leveraging it to improve model performance, for example, by enhancing feature extraction in multi-view stereo or creating more robust face recognition systems. This research is significant because it addresses limitations of existing symmetric models and opens avenues for developing more efficient and powerful computational systems across various applications.