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  • Review Article
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Integrated photonic neuromorphic computing: opportunities and challenges

Abstract

Using photons in lieu of electrons to process information has been an exciting technological prospect for decades. Optical computing is gaining renewed enthusiasm, owing to the accumulated maturity of photonic integrated circuits and the pressing need for faster processing to cope with data generated by artificial intelligence. In neuromorphic photonics, the bosonic nature of light is exploited for high-speed, densely multiplexed linear operations, whereas the superior computing modalities of biological neurons are imitated to accelerate computations. Here, we provide an overview of recent advances in integrated synaptic optical devices and on-chip photonic neural networks focusing on the location in the architecture at which the optical to electrical conversion takes place. We present challenges associated with electro-optical conversions, implementations of optical nonlinearity, amplification and processing in the time domain, and we identify promising emerging photonic neuromorphic hardware.

Key points

  • In this Review, photonic computing hardware is broadly categorized based on the depth at which opto-electronic conversions take place. These include (1) incoherent electro-optical processors, (2) coherent electro-optical processors and (3) all-optical neural networks including photonic spiking neural networks (SNNs).

  • Looking into the future of photonic computing, we project that coherent and incoherent approaches alike have the potential to surpass state-of-the-art electronic hardware (for example, Google TPU v4) by two orders of magnitude in throughput, power efficiency and compute density.

  • Three-dimensional photonic components produced by 3D printing technologies are bridging free-space and in-fibre photonic neural networks with integrated photonic accelerators, providing an exciting outlook into the future.

  • Although the field of integrated photonic SNNs is still at its infancy, these are particularly well suited to photonics, which is not limited by the capacitive effects present in their electronic counterparts. As the individual devices are only active during spiking, SNNs offer energy-efficient alternative computing modality.

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Fig. 1: Neuromorphic computing schemes based on the depth of electro-optical conversions.
Fig. 2: Photonic accelerators based on incoherent accumulation.
Fig. 3: Photonic accelerators based on coherence.
Fig. 4: Optical nonlinearity implementations in photonic artificial neural networks.
Fig. 5: Spiking and photonic spiking neural networks.
Fig. 6: Scaling and performance analysis of typical photonic accelerator architecture.

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Acknowledgements

The authors acknowledge discussions with A.Ne. This work has received funding from the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement Nos 101098717 (Hybrain Project), 101017237 (PHOENICS Project) and 101098717 (RESPITE Project). This research was also supported by EPSRC via grants EP/W022931/1 and EP/W034387/1.

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N.F., B.D. and H.B. researched the data, wrote the manuscript and contributed substantially to discussion of the content. H.B. led the project. All authors reviewed and edited the manuscript.

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Correspondence to Harish Bhaskaran.

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Farmakidis, N., Dong, B. & Bhaskaran, H. Integrated photonic neuromorphic computing: opportunities and challenges. Nat Rev Electr Eng 1, 358–373 (2024). https://doi.org/10.1038/s44287-024-00050-9

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