Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices

Abstract

Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of “in-sensor computing”. This is a promising concept associated with the development of compact and low-power machine vision devices, which is crucial important for bionic prostheses of eyes, on-board image recognition systems for unmanned vehicles, computer vision in robotics, etc. This concept can be applied for the creation of a memristor based neuromorphic analog machine vision systems, and here, we propose a new architecture for these systems in which captured visual data are fed to a spiking artificial neural network (SNN) based on memristive devices without analog-to-digital and digital-to-analog conversions. Such an approach opens up the opportunities of creating more compact, energy-efficient visual processing units for wearable, on-board, and embedded electronics for such areas as robotics, the Internet of Things, and neuroprosthetics, as well as other practical applications in the field of artificial intelligence.

Keywords

• neuromorphic systems
• memristive devices
• machine vision
• artificial intelligence
• artificial neural networks
• spiking neural networks