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Superconducting Optoelectronic Circuits for Neuromorphic Computing

Published

Author(s)

Jeffrey M. Shainline, Sonia M. Buckley, Richard P. Mirin, Sae Woo Nam

Abstract

We propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes the requirement for time-multiplexing that has limited purely electronic platforms from achieving event rates above 1 kHz. The proposed processing units can operate at 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm^2 for neurons with 700 connections operating at full speed at 2K. The processing units achieve an energy efficiency of 20 aJ per synapse event, an improvement of roughly six orders of magnitude over recent CMOS demonstrations. By leveraging multilayer photonics with low-temperature-deposited waveguides and superconductors with feature sizes > 100 nm, this approach could scale to massive interconnectivity near that of the human brain, and could surpass the brain in speed and efficiency.
Citation
Physical Review Applied
Volume
7

Keywords

Superconductive devices, integrated photonics, single-photon detectors, Josephson junctions, single-flux quantum, neural networks, neuromorphic computing
Created March 23, 2017, Updated November 10, 2018