# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 1 Introduction  
The desire to create machines that are capable of seeing the world around them the way that people see it has motivated the development of computer vision systems for many years. An interesting fact is that the first neurocomputer in the history of mankind, created by [PERSON] in 1958 [1], was designed specifically to solve computer vision problems--in particular, to recognize the letters of the English alphabet. The work of [PERSON] and many other pioneers in the field of neural network theory, more than half a century ago, introduced the high potential of artificial neural networks (ANNs) for solving computer vision problems; but, at that time, they did not receive intensive development for a number of reasons and were forgotten for many years, known in history as the winter of artificial intelligence.  
Currently, computer vision systems are designed in accordance with the conventional principles of creating computing systems with the von Neumann architecture. They have digital processor units (e.g., the RISC (reduced instruction set computer), ARM (advanced RISC machine), or VLIW (very long instruction word) instruction set architectures for DSP (digital signal processor) and VPU (vision processing unit) microprocessors or GPUs (graphics processing units)) for processing visual information, memories for storing commands and data, as well as input devices in the form of photosensors (e.g., CCD (charge-coupled device) or APS (active-pixel sensor) image sensors) with analog-to-digital converters. During the operation of such a system, the captured image of a scene from the outside world undergoes digitization, encoding, software preprocessing, and processing using a machinelearning model like ANNs (e.g., ResNet, VGG, Yolo, etc.), which involves storing a large number of parameters of the model itself and making a huge number of memory requests during the model's inference on digital processor units with serial principles of data processing (albeit multicore). Such systems require the use of specialized computers, which makes them complex, thus consuming a lot of energy and making them expensive.  
Analyzing the published reviews [2; 3], one can find out that memristor-based systems have advantages over modern transistor-based digital processing units in tasks related to the hardware implementation of ANNs. In particular, existing memristor-based chips make it possible to solve image processing problems with a high accuracy, while consuming two to three orders of magnitude less energy than GPUs and digital neuromorphic processors and using the chip area more efficiently. So, the authors of these papers have shown that matrix multiplication, which is the most frequent operation in ANN algorithms, can be performed in an analog form based on [PERSON]'s and Kirchhoff's laws with a high level of parallelism, and this makes it possible to create systems that have a high performance and speed and consume little energy.  
At the same time, the need for analog-to-digital and digital-to-analog conversions minimizes the potential energy gain from using memristors. Due to the fact that memristive devices open up opportunities for creating neuromorphic systems in which all processing takes place in an analog form, it seems reasonable to exclude analog-to-digital and digital-to-analog conversions from machine vision systems. In this case, the signals from the photosensors can be fed to a memristor-based ANN and processed without digitization, because in the pretrained ANNs, the memristor's conductivities form the model of visual information processing and simultaneously perform this processing. Moreover, if we fed visual signals directly to a memristor-based SNN, its training could be performed during the operation like in biological systems. In this paper, we propose a new architecture of memristors based on neuromorphic analog machine vision systems, relying on these musings.