Implementing an Insect Brain Computational Circuit Using III-V Nanowire Components in a Single Shared Waveguide Optical Network
(2020) In ACS Photonics 7(10). p.2787-2798- Abstract
Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for subwavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation, and emission of the optical signals are performed by neuron-like nodes constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption... (More)
Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for subwavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation, and emission of the optical signals are performed by neuron-like nodes constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network using previously experimentally derived parameters. The results are used as input in the full model, and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.
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- author
- Winge, David O. LU ; Limpert, Steven LU ; Linke, Heiner LU ; Borgström, Magnus T. LU ; Webb, Barbara ; Heinze, Stanley LU and Mikkelsen, Anders LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- insect brain, nanowire, neural network, optical interconnects, optoelectronic device, optoelectronic modeling, phototransistor, polarization anisotropy
- in
- ACS Photonics
- volume
- 7
- issue
- 10
- pages
- 12 pages
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- scopus:85096460680
- pmid:33123615
- ISSN
- 2330-4022
- DOI
- 10.1021/acsphotonics.0c01003
- language
- English
- LU publication?
- yes
- id
- 9f07014b-7ea6-4c45-8374-cbc7e081df52
- date added to LUP
- 2020-12-01 12:24:48
- date last changed
- 2024-09-05 10:09:22
@article{9f07014b-7ea6-4c45-8374-cbc7e081df52, abstract = {{<p>Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for subwavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation, and emission of the optical signals are performed by neuron-like nodes constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network using previously experimentally derived parameters. The results are used as input in the full model, and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.</p>}}, author = {{Winge, David O. and Limpert, Steven and Linke, Heiner and Borgström, Magnus T. and Webb, Barbara and Heinze, Stanley and Mikkelsen, Anders}}, issn = {{2330-4022}}, keywords = {{insect brain; nanowire; neural network; optical interconnects; optoelectronic device; optoelectronic modeling; phototransistor; polarization anisotropy}}, language = {{eng}}, number = {{10}}, pages = {{2787--2798}}, publisher = {{The American Chemical Society (ACS)}}, series = {{ACS Photonics}}, title = {{Implementing an Insect Brain Computational Circuit Using III-V Nanowire Components in a Single Shared Waveguide Optical Network}}, url = {{http://dx.doi.org/10.1021/acsphotonics.0c01003}}, doi = {{10.1021/acsphotonics.0c01003}}, volume = {{7}}, year = {{2020}}, }