Memristor-Emulator-Based Crossbar Array for Object Detection and Recognition

Abstract

Object detection and recognition are crucial for autonomous vehicles, surveillance systems, and human-computer interaction. We present a new fully complementary metal-oxide semiconductor (CMOS) circuit-based system for object detection and recognition using a Spiking Neural Network (SNN). Our holistic CMOS circuit integrates neuromorphic elements, including a leaky integrate-and-fire (LIF) neuron model, spike time-dependent plasticity (STDP) memristor synapse, and basic analog and digital building blocks. The learning mechanism is manifested by a completely different approach based on an array of XOR gates to recognize six different objects with $256 \times 6$ size crossbar arrays. This is the first-ever recognition mechanism of its kind. We also perform handwritten digit recognition using a $64 \times 4$ size array using grayscale conversion. The proposed system’s robustness is validated through process corner simulations, noise analysis, and temperature analysis. We also show the accuracy of our design for the digit recognition task using a confusion matrix plot, and the accuracy turns out to be 82.5 %. Our pioneering approach using a CMOS memristor-emulator STDP crosspoint array-based architecture achieves minimal energy consumption per neuron block, which amounts to $\approx ~2.59$ pJ per neuron block and an overall energy budget of 663.66 pJ for the entire system considering the object recognition task.