Alex Pappachen James Alex Pappachen James

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66583

### **Abstract**

One of the possible approaches to achieve more than Moore's law with signal process‐ ing circuits is to inspire from functioning of human brain to mimic neural functions by exploring emerging technologies such as memristor circuits. While fast Fourier transform (FFT) implementations are largely based on CMOS gates, they are limited by the com‐ putation speed and availability limits on the number of Boolean variables it can handle at a given time. Biological neurons and networks on the other hand are generalized in nature and can handle both analogue and digital signals. Through this chapter, memris‐ tor‐based resistive threshold logic family of gates that inspire from brain‐like large vari‐ able logic functions is introduced. This logic consists of a memristors acting as weights to the inputs followed by threshold operations emulating neuronal synapse. Using this Boolean logic, a processing unit that can compute Fourier transform of a given set of inputs was developed. Various comparisons of the circuit are found to be advantageous in implementing neuromorphic circuits. The existing logic families were carried out and the proposed logic family was found too advantageous in many ways.

**Keywords:** memristors, threshold logic, circuits, Fourier, FFT
