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By imitating the synaptic connectivity and plasticity of the brain, emergingelectronic nanodevices offer new opportunities as the building blocks ofneuromorphic systems. One challenge for largescale simulations of computationalarchitectures based on emerging devices is to accurately capture deviceresponse, hysteresis, noise, and the covariance structure in the temporaldomain as well as between the different device parameters. We address thischallenge with a high throughput generative model for synaptic arrays that isbased on a recently available type of electrical measurement data for resistivememory cells. We map this real world data onto a vector autoregressivestochastic process to accurately reproduce the device parameters and theircross-correlation structure. While closely matching the measured data, ourmodel is still very fast; we provide parallelized implementations for both CPUsand GPUs and demonstrate array sizes above one billion cells and throughputsexceeding one hundred million weight updates per second, above the pixel rateof a 30 frames/s 4K video stream.

A High Throughput Generative Vector Autoregression Model for Stochastic Synapses