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Towards “smart transceivers” in FPGA-controlled lithium-niobate-on-insulator integrated circuits for edge computing applications (Invited)

In the realm of advanced computing and signal processing, the need for optimized data processing methodologies is steadily increasing. With the world producing vast quantities of data, computing architectures necessitate to be swifter and more energy efficient. Edge computing architectures such as t...

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Main Authors: Bankwitz, Julian Rasmus (Author) , Dijkstra, Jelle (Author) , Pradip, Ravi (Author) , McRae, Liam (Author) , Lomonte, Emma (Author) , Lenzini, Francesco (Author) , Pernice, Wolfram (Author)
Format: Article (Journal)
Language:English
Published: 30 Nov 2023
In: Optical materials express
Year: 2023, Volume: 13, Issue: 12, Pages: 3667-3676
ISSN:2159-3930
DOI:10.1364/OME.503340
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1364/OME.503340
Verlag, lizenzpflichtig, Volltext: https://opg.optica.org/ome/abstract.cfm?uri=ome-13-12-3667
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Author Notes:J. Rasmus Bankwitz, Jelle Dijkstra, Ravi Pradip, Liam McRae, Emma Lomonte, Francesco Lenzini, Wolfram H. P. Pernice

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520 |a In the realm of advanced computing and signal processing, the need for optimized data processing methodologies is steadily increasing. With the world producing vast quantities of data, computing architectures necessitate to be swifter and more energy efficient. Edge computing architectures such as the NetCast architecture [1] combine the strength of electronic and photonic computing by outsourcing multiply-accumulate operations (MAC) to the optical domain. Herein we demonstrate a hybrid architecture, combining the advantages of FPGA data processing facilitating an ultra-low power electro-optical “smart transceiver” comprised of a lithium-niobate on insulator photonic circuit. The as-demonstrated device combines potential GHz speed data processing, with a power consumption in the order of 6.63 fJ per bit. Our device provides a blueprint of a unit cell for a TFLN smart transceiver alongside a variety of optical computing architectures, such as optical neural networks, as it provides a low power, reconfigurable memory unit. 
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