# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 2 State-of-the-Art Studies  
### Memristor-Based Systems for Machine Vision  
The memristor, as the fourth passive element of electrical circuits, was proposed in 1971 by Prof. [PERSON] [4]. A little later [5], in 1976, Prof. [PERSON] proposed a generalized definition of the memristor and described it with a port equation equivalent to Ohm's law and a set of state equations that describe the dynamics of the internal state variables. The start of the active study of memristors and systems based on these equations can be considered to be in 2008, when the article [6] was published. Currently, memristors are used to create computer memories ReRAM (Resistive Random Access Memory) [7] and CAM (Content-Addressable Memory) [8], hardware implementation of ANNs [9; 10], and neuromorphic systems [11; 12] (\"in-memory computing\"). It is important to note that a main feature of these devices is that the signals inside them are processed in an analog form. The authors of article [13] were among the first to propose an image recognition system based on passive crossbar arrays of 20 \(\times\) 20 memristors [13]. This is a multilayer feedforward ANN, trained to recognize images of letters of the Latin alphabet with an accuracy of approximately 97%. This work has shown the potential of using such technologies in the field of creating machine vision systems. This is also confirmed by significant advances in the use of memristors for pattern recognition via convolutional ANNs, reservoir computing models, and ANNs with artificial dendrites, as demonstrated in [14; 15; 16; 17]. For example, popular models including VGG-16 and MobileNet have been successfully implemented and tested on an ImageNet dataset [14] and it was shown that for memristor-based ANNs, the power consumption is more than three orders of magnitude lower than that of a central processing unit and is 70 times lower than that of a typical application-specific integrated circuit chip [17]. In addition to traditional ANN architectures based on memristors, the authors of articles [18; 19] presented and shared a human retina simulator that can be used in the development of promising variants of analog vision systems, among others. The hardware implementation of a Hopfield network based on memristor chips and the result of its operation as an associative memory is described in article [20]. The authors have shown that by using both asynchronous and synchronous refresh schemes, complete-emotion images can be recalled from partial information. In [21], the authors demonstrated the processing of a video stream in real time with the selection of object boundaries using a 3D array of memristive devices based on HfO\({}_{2}\), which is designed for programming the binary weights of four convolutional filters and parallel processing of input images. Their achievements in the field of hardware implementation of ANNs for a wide range of image processing tasks based on arrays of memristive devices with a size of 128 \(\times\) 64 are presented in [22; 23], and they have also shown that such devices are several times superior to graphics and signal processors in terms of speed and low power consumption. The results of comparing systems based on memristors with modern ANN hardware accelerators based on transistors for various indicators are given in the reviews [2; 24; 25; 26; 27; 2; 2; 28]. It can be seen from these articles that despite the analog principles of information processing, the prototypes of such systems provide high accuracy of the inference of the ANN models in image recognition tasks; for example, the NeuRRAM chip [29] provides a 99% accuracy on the MNIST dataset and an 85.7% accuracy on the CIFAR-10 dataset, 1 Mb resistive random-access memory (ReRAM) nvCIM macro [30] provides an inference accuracy of 98.8% on the MNIST dataset, 2 Mb nvCIM macro [31] provides a 90.88 % accuracy on the CIFAR-10 dataset for ResNet-20 and a 65.71 % accuracy on the CIFAR-100 dataset for ResNet-20, etc.