Discover the latest publication from UAB on neuromorphic circuit simulation using the compact model developed within WAKeMeUP for OxRAM devices!

Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large dataset Pattern Recognition

F. L. Aguirre et al. , IEEE Access, vol. 8, 202174 (2020)

DOI: 10.1109/ACCESS.2020.3035638

UAB's team investigates the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua’s memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. UAB's team shows that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.