Neuromorphic Computing Using Memristor Synapses and CMOS Neurons

To address the increasing demands of artificial intelligence applications and the limitations of traditional computing, such as high power consumption, limited scalability, and inadequate parallelism, neuromorphic computing systems have been developed. This paper introduces a neuromorphic computing system designed for robust digit pattern recognition. A 20x20 memristor array-based synapse circuit, integrated with a refined Axon-Hillock (A-H) neuron model, forms the foundation to emulate synaptic and neuronal dynamics. A hard-coded approach is employed to adjust the synaptic weights of the memristor array for recognising digit patterns from 1 to 9. The results demonstrate that the neuromorphic computing system can accurately recognise the input patterns. Notably, the system is capable of maintaining its recognition abilities with 5% noise interference.