Mohammad Bavandpour - Artificial Creatures Lab, Electrical Engineering School, Sharif University of Technology, Tehran, Iran
Saeed Bagheri Shouraki - Artificial Creatures Lab, Electrical Engineering School, Sharif University of Technology, Tehran, Iran,
Hamid Soleimani - Electrical Engineering Department, Razi University, Kermanshah, Iran
Arash Ahmadi

Memristive devices have gained significant research attention lately because of their unique properties and wide application spectrum. In particular, memristor-based resistiverandom access memory (RRAM) offers the high density, low power, and low volatility required for next-generation nonvolatile memory. Nowadays, despite significant advances inhardware technology, in the case of massively parallel systems still new computational architectures are required. Simulation oflarge quantity of memristors in the crossbar structure is a known challenge encountering these barriers. Using graphic processingunits (GPU) as a low-cost and high-performance computing platform is an efficient preferred approach to this problem. Inthis paper, we demonstrate an RRAM simulator that runs on a single GPU. The GPU-RRAM model (running on an NVIDIA GT325M with 1GB of memory) is up to 50 times faster than a CPU version. Besides a limitation on simulation of the memristor in the crossbar structure has been seen when more than 10 thousand of them are simulated but GPU can simulate more than one hundred million ones

Memristor, Resistive Random Access Memory (RRAM), Graghic Processing Unit (GPU)