A Fourier neuromorphic visual system based on InGaZnO synaptic transistor

Abstract

The hierarchical structure of the biological visual system enables multilevel features of sensory stimuli to be pre-extracted before being transmitted to the nerve center, rendering the remarkable ability to perceive, filter, categorize, and identify targets in complex environments. However, it is a challenge to resemble such extraction capability with respect to spatial features in a neuromorphic visual system. In this Letter, we propose an indium-gallium-zinc-oxide synaptic transistor-based Fourier neuromorphic visual system for image style classifying. The images are transformed into the frequency domain through an optic Fourier system, greatly reducing energy and time dissipation in comparison with numerical computation. Then, the transformed information is coded into spike trains, which are nonlinearly filtered by synaptic transistors. The energy consumption for this filtering process is estimated to be ∼1.28 nJ/pixel. The features of drawing style could be enhanced through the filtering process, which facilitates the followed pattern recognition. The recognition accuracy in classifying stylized images is significantly improved to 92% through such Fourier transform and filtering process. This work would be of profound implications for advancing neuromorphic visual system with Fourier optics enhanced feature extraction capabilities.