# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 3 The System's Architecture
With the correct selection of the load resistance R\({}_{\text{load}}\) of the input channel, the maximum amplitude of the operating voltage (the maximum voltage during the inference) will not be greater than the threshold voltage of the memristor. The input channel for the spiking ANN (Figure 3B) additionally has an integrator with a threshold. The integrator's task is to accumulate charge and generate a pulse of unit amplitude at those moments of operation when the accumulated charge in the input channel exceeds the threshold value. Thus, in the input channels, the signal amplitude is converted into frequency, or, more precisely, the illumination is converted into the pulse frequency. The higher the illumination of the input channel, the higher the frequency of voltage pulses supplied to the ANN. In this case, the processing of one visual image (inference) will no longer be performed in one clock cycle, but over a given time interval during which the shutter of the optical system is open. Regardless of the input channel circuit option, the signal encoding visual information is fed to the neural part without digitization. In the neural part, memristors act as synapses. Moreover, memristors make it possible to implement in hardware the synapses of traditional formal ANN architectures (multilayer perceptron [13], Hopfield network [20], deep ANNs [14; 70], convolutional ANNs [16; 24; 71], LSTM networks [72], etc. ), in which input information is multiplied by a pre-programmed weight, and synapses for spiking ANNs, in which the memristor exhibits mechanisms of synaptic plasticity similar to living BNNs [73; 74]. In the simplest case, one synapse and the operation of scalar multiplication of two numbers can be implemented on one memristor using [PERSON]'s law (Figure 4A). For the input value x, it is necessary to set the equivalent input voltage V\({}_{\text{in}}\) and apply it to a circuit with a memristor having a pre-recorded resistance \(\text{R}_{\text{m}}\). The resistance \(\text{R}_{\text{m}}\) in memristive devices can be changed within the boundaries of the minimum (LRS or R\({}_{\text{min}}\))- and maximum (HRS or R\({}_{\text{max}}\))-possible resistances. Then, a current I will flow in the circuit, which can be converted into an output voltage V\({}_{\text{out}}\), for example, through a load resistor or a transimpedance amplifier (TIA). The resistance of the load resistor \(\text{R}_{\text{L}}\) or the negative feedback resistance of the TIA \(\text{R}_{\text{f}}\), together with the memristor resistance \(\text{R}_{\text{m}}\) will form the weight of the synapse. With this approach, only unipolar weights can be implemented, since the resistance cannot be negative. For bipolar weights, it is necessary to use circuits with two or, for example, four [75] memristors (Figure 4B-D). In the variants of circuits presented in Figure 4, the weights of the neuron synapses will already have bipolar values due to the difference in several resistances of the memristive devices. In the circuit in Figure 4B, this is achieved, in particular through the use of an additional differential amplifier, the output of which is the difference in voltage obtained by converting the current in each branch containing the memristor. In the circuit in Figure 4C, this is achieved by duplicating the inputs, one of which is inverse. In the circuit in Figure 4D, the output voltage is differentially taken from two voltage dividers formed by four memristors. The shape of the graph of the dependence of weight on resistance for the first case is hyperbolic, and for the second, it is linear. Using the circuits of the input channels (Figure 3) and synapses (Figure 4), it is possible to create various variants of formal and spiking ANNs for machine vision systems. Figure 5A shows a variant of the formal ANN. The light falls on the photodiodes of the input channels and forms a different amplitude of the input signals \(V_{in}\) depending on the illumination.