Ultrafast neural sampling with spiking nanolasers

Abstract

Owing to their significant advantages in terms of bandwidth, power efficiency, and latency, optical neuromorphic systems have arisen as interesting alternatives to digital electronic devices. Recently, photonic crystal nanolasers with excitable behavior were first demonstrated. Depending on the pumping strength, they emit short optical pulses -- spikes -- at various intervals on a nanosecond timescale. In this theoretical work, we show how networks of such photonic spiking neurons can be used for Bayesian inference through sampling from learned probability distributions. We provide a detailed derivation of translation rules from conventional sampling networks, such as Boltzmann machines, to photonic spiking networks and demonstrate their functionality across a range of generative tasks. Finally, we provide estimates of processing speed and power consumption, for which we expect improvements of several orders of magnitude over current state-of-the-art neuromorphic systems.

18 pages, 8 figures

References in corpus (18)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Photonics for artificial intelligence and neuromorphic computing
• A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
• Deep Learning with Coherent VCSEL Neural Networks
• Six networks on a universal neuromorphic computing substrate
• Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
• Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks
• Stochastic inference with spiking neurons in the high-conductance state
• Stochastic Simulator for modeling the transition to lasing
• Higher-order photon correlations in pulsed photonic crystal nanolasers
• Accelerated physical emulation of Bayesian inference in spiking neural networks
• Electrically-driven Photonic Crystal Lasers with Ultra-low Threshold
• Dynamical properties of nanolasers based on few discrete emitters
• Efficient stochastic simulation of rate equations and photon statistics of nanolasers
• Experimental reservoir computing with diffractively coupled VCSELs
• Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
• Spiking neuromorphic chip learns entangled quantum states
• Scalable construction of hybrid quantum photonic cavities