2025/03/14 by Lina Jaurigue, Jaurigue, Lina, Kathy Lüdge +1
Computer Science · Engineering · #Advanced Memory and Neural Computing #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Neural Networks and Reservoir Computing
paper · pdf · doi:10.48550/arxiv.2503.11179
openalex publication_date 2025/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Physical systems exhibiting hysteresis are increasingly being used in neuromorphic and in-memory computing research. Generally, the resistance switching of devices with rate-independent hysteresis are being investigated for their use as trainable weights in neural networks, whereas the dynamics of devices showing rate-dependent hysteresis are being investigate for their potential as nodes in, for example in reservoir computing systems. In our work we instead show the computing potential of a simple rate-independent hysteresis system. We show that by driving a system of only two linear branches with time-multiplexed inputs it is possible to generate nonlinear transforms and perform timeseries prediction tasks.