TY - GEN A1 - Dersch, Nadine A1 - Roemer, Christian A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Schwarz, Mike A1 - Iñíguez, Benjamín A1 - Kloes, Alexander T1 - Fast circuit simulation of memristive crossbar arrays with bimodal stochastic synaptic weights T2 - 2024 IEEE Latin American Electron Devices Conference (LAEDC) N2 - This paper presents an approach for highly efficient circuit simulation of hardware-based artificial neural networks by using memristive crossbar array architectures. There are already possibilities to test neural networks with stochastic weights via simulations like the macro model NeuroSim. However, the noise-based variability approach offers more realistic setting options including elements of a classical circuit simulation for more precise analysis of neural networks. With this approach, statistical parameter fluctuations can be simulated based on different distribution functions of devices. In Cadence Virtuoso, a simulation of a crossbar array with 10 synaptic weights following a bimodal distribution, the new approach shows a 1,000x speedup compared to a Monte Carlo simulation. Initial tests of a memristive crossbar array with over 15,000 stochastic weights to classify the MNIST dataset show that the new approach can be used to test the functionality of hardware-based neural networks. KW - RRAM Y1 - 2024 SN - 979-8-3503-6130-8 U6 - https://doi.org/10.1109/LAEDC61552.2024.10555829 SN - 979-8-3503-6129-2 SN - 2835-3471 SP - 1 EP - 4 PB - IEEE ER -