Highly scalable neuromorphic hardware with 1-bit stochastic nano-synapses

Abstract

Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuation on switching probability of emerging magnetic memory are probabilistic phenomena in nature. Therefore, process of binary switching in these nonvolatile memories are considered stochastic that varies from switching-to-switching. Moreover, position-dependent, spatially correlated, and distance-dependent variation in these electron devices, like advanced CMOS processes, provide rich in-situ spatiotemporal stochastic characteristics. Based on a partial characterization of the switching variation, this preliminary work presents highly scalable neuromorphic hardware based on crossbar array of 1-bit resistive elements as distributed stochastic synapses. The network shows the ability to emulate selectivity of synaptic potentials in neurons of primary visual cortex to the orientation of a visual image. The proposed model could be configured to accept a wide range of emerging non-volatile memory technologies