Neuromorphic computing with integrated photonics and superconductors

Abstract

We present a hardware platform combining integrated photonics with superconducting electronics for large-scale neuromorphic computing. Semiconducting few-photon light-emitting diodes work in conjunction with superconducting-nanowire single-photon detectors to behave as spiking neurons. These neurons are connected through a network of waveguides, and variable weights of connection can be implemented using several approaches. These processing units can operate at 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm2. The processing units achieve an energy efficiency of 20 aJ/synapse event, an improvement of six orders of magnitude over recent CMOS demonstrations [1]. We present calculations showing this approach could scale to interconnectivity near that of the human brain, and could surpass the brain in speed and efficiency.