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.

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

Lenzini, Francesco;
2023

Abstract

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.
2023
Istituto di fotonica e nanotecnologie - IFN - Sede Milano
Optical information processing, Optical neural networks, Integrated photonics, Thin-film lithium niobate
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/533793
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