Highly Scalable Neuromorphic Hardware with 1-bit Stochastic nano-Synapses

TL;DR

This paper introduces a highly scalable neuromorphic hardware architecture utilizing 1-bit stochastic resistive synapses, demonstrating robust visual cortex emulation and adaptability to various nanodevices.

Contribution

It presents a novel scalable neuromorphic system based on stochastic resistive crosspoints, capable of emulating visual cortex selectivity.

Findings

Robust performance in visual cortex emulation

Compatible with various nanodevices

Scalable crossbar array architecture

Abstract

Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuations on switching probability of emerging magnetic switches are probabilistic phenomena in nature, and thus, processes of binary switching in these nonvolatile memories are stochastic and vary from switching cycle-to-switching cycle, in the same device, and from device-to-device, hence, they provide a rich in-situ spatiotemporal stochastic characteristic. This work presents a highly scalable neuromorphic hardware based on crossbar array of 1-bit resistive crosspoints as distributed stochastic synapses. The network shows a robust performance in emulating 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 nanodevices.