Resonant tunnelling diode nano-optoelectronic spiking nodes for neuromorphic information processing
This work addresses the need for efficient neuromorphic computing hardware, offering a domain-specific advancement with potential for high-speed processing.
The authors tackled the challenge of creating high-speed, low-energy neuromorphic hardware by introducing an optoelectronic spiking neuron based on resonant tunnelling diodes, achieving ultrafast operation at about 100 ps per spike and low energy consumption below pJ per spike.
In this work, we introduce an optoelectronic spiking artificial neuron capable of operating at ultrafast rates ($\approx$ 100 ps/optical spike) and with low energy consumption ($<$ pJ/spike). The proposed system combines an excitable resonant tunnelling diode (RTD) element exhibiting negative differential conductance, coupled to a nanoscale light source (forming a master node) or a photodetector (forming a receiver node). We study numerically the spiking dynamical responses and information propagation functionality of an interconnected master-receiver RTD node system. Using the key functionality of pulse thresholding and integration, we utilize a single node to classify sequential pulse patterns and perform convolutional functionality for image feature (edge) recognition. We also demonstrate an optically-interconnected spiking neural network model for processing of spatiotemporal data at over 10 Gbps with high inference accuracy. Finally, we demonstrate an off-chip supervised learning approach utilizing spike-timing dependent plasticity for the RTD-enabled photonic spiking neural network. These results demonstrate the potential and viability of RTD spiking nodes for low footprint, low energy, high-speed optoelectronic realization of neuromorphic hardware.