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- W3202036733 abstract "Spiking neural networks are known to be superior over artificial neural networks for their computational power efficiency and noise robustness. The benefits of spiking coupled with the high-bandwidth and low-latency of photonics can enable highly-efficient, noise-robust, high-speed neural processors. The landscape of photonic spiking neurons consists of an overwhelming majority of excitable lasers and a few demonstrations on nonlinear optical cavities. The silicon platform is best poised to host a scalable photonic technology given its CMOS-compatibility and low optical loss. Here, we present a survey of existing photonic spiking neurons, and propose a novel spiking neuron based on a hybrid graphene-on-silicon microring cavity. A comparison among a representative sample of photonic spiking devices is also presented. Finally, we discuss methods employed in training spiking neural networks, their challenges as well as the application domain that can be enabled by photonic spiking neural hardware." @default.
- W3202036733 created "2021-10-11" @default.
- W3202036733 creator A5019886826 @default.
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- W3202036733 creator A5061734623 @default.
- W3202036733 creator A5082737646 @default.
- W3202036733 date "2021-09-28" @default.
- W3202036733 modified "2023-09-27" @default.
- W3202036733 title "Photonic spiking neural networks and CMOS-compatible graphene-on-silicon spiking neurons." @default.
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