# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 2 State-of-the-Art Studies
### Memristor-Based Systems for Machine Vision
In order to provide compatibility with modern digital IT infrastructure, systems based on memristive devices must have interfaces that make it possible to receive digital input data and return the results of their analog processing in digital form. For this reason, all the above prototypes of neuromorphic systems based on memristive devices [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] contain analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to interact with digital devices. Such sets of DACs and ADCs can become the bottleneck of memristor-based computing system architectures, like the computer memory bus is the bottleneck of the von Neumann architecture, which reduces the potential benefits of their applications for solving specific practical problems set before existing computers. For example, if the system uses a 16 \(\times\) 16 memristor crossbar array, then 256 scalar multiplications can be performed in one clock cycle, provided that all 16 input values are supplied simultaneously. At the same time, in order to push the inputs into the DAC and then pull the outputs from the ADC it is necessary to take at least 32 clock cycles, which is disproportionately more than the requirements of performing matrix multiplication on the crossbar array. That is why an actual area of research in this field is the development of architectures in which there can be as few signal conversions as possible. For example, the importance of developing such concepts was stated by the authors of article [8] in 2020, who proposed an analog CAM circuit taking advantage of the analog memristor conductance tunability for the first time. It enables the processing of analog sensor data without the need for an analog-to-digital conversion step. In their study, a practical circuit implementation composed of six transistors and two memristors was demonstrated in both an experiment and a simulation. The authors note that the analog capability opens up the possibility for directly processing analog signals acquired from sensors, and is particularly attractive for Internet of Things applications due to the potential low power and energy footprint. The idea of fully analog computing is not new and existed long before the invention of memristive devices in 2008, and even before their theoretical description in the 70s. For example, the US patent [33] published in 2006 discloses an analog CAM described in 1991 [34] that employs analog storage cells with programmable analog transfer function capabilities. The use of electrically erasable programmable read-only memory (EEPROM) cells in an analog storage device avoids the need to convert an analog waveform into a digital representation, reducing the complexity embodied in an integrated circuit as well as decreasing the die dimension. An earlier well-known example of a fully analog computer is \"Sceptron\"--the device for analog signal recognition, developed in 1962 [35]. Sceptron not only performed a function that would require hundreds of conventional filters and a large storage capacity at that time, but it ran a program itself for recognizing a complex signal without detailed knowledge of its characteristics. If one considers analog computers with neural network architecture, then one of the most famous is the adaptive Adaline neuron, implemented in 1962 [36] using \"memistors\" (not to be confused with \"memristors\"). With such an element it was possible to obtain an electronically variable gain control, along with the memory required for storage of the system's experiences of training. The authors created a neuron capable of training and recognizing 3 \(\times\) 3 patterns of the letters of the English alphabet. Of course, the earliest example of a fully analog ANN is the Mark-1 neurocomputer created by [PERSON] [1] in 1958, which was mentioned above. Due to the fact that the main goal of machine vision systems is not to capture and save images, but to obtain information about objects in the field of vision (e.g., class, segment, id., etc.