  By imitating the synaptic connectivity and plasticity of the brain, emerging
electronic nanodevices offer new opportunities as the building blocks of
neuromorphic systems. One challenge for largescale simulations of computational
architectures based on emerging devices is to accurately capture device
response, hysteresis, noise, and the covariance structure in the temporal
domain as well as between the different device parameters. We address this
challenge with a high throughput generative model for synaptic arrays that is
based on a recently available type of electrical measurement data for resistive
memory cells. We map this real world data onto a vector autoregressive
stochastic process to accurately reproduce the device parameters and their
cross-correlation structure. While closely matching the measured data, our
model is still very fast; we provide parallelized implementations for both CPUs
and GPUs and demonstrate array sizes above one billion cells and throughputs
exceeding one hundred million weight updates per second, above the pixel rate
of a 30 frames/s 4K video stream.
