Photonic-digital hybrid artificial intelligence hardware architectures: at the interface of the real and virtual worlds

abstract

Artificial intelligence (AI) technology has become an undeniable presence in society, as seen in the growing use of chatbots (e.g. ChatGPT) and recent Nobel Prize awards. However, the challenge of developing AI hardware architectures that efficiently integrate real-world analog signals with digital computational frameworks remains unresolved. This is particularly true for optical and neuromorphic computing systems. This work reports the implementation of an optical computing module in two hybrid AI architectures. The key element of the module is a photonic layer comprised of nanometer-scale spherical carbon dots. When excited with light-emitting diodes, the photonic layer exhibits a complex optical response. This layer is a feasible building block for two hybrid AI architectures: one based on optical processing and the other on the principles of neuromorphic computing. The modality of operation of the photonic layer is that it converts numerical input into a complex emission space, the results of which are processed using a Gaussian process model. Two systems based on this building block have been tested on a real-world dataset. They outperform conventional digital-only models, with coefficients of determination of r2 = 0.90 and r2 = 0.84 (training) and r2 = 0.85 and r2 = 0.87 (testing) for the optical and neuromorphic architectures, respectively. The systems discussed in this work serve as interfaces that bridge the real and virtual worlds. They offer exceptional optical properties, relative innocuity, low toxicity, stability, cost-effectiveness, seamless scalability, and robustness. The proposed architectures push the boundaries of advanced hybrid designs.

subject category

Optics; Physics

authors

Dias, LMS; Abranches, DO; Bastos, AR; Fu, LS; Neto, ANC; Pereira, RFP; Bermudez, VD; Towe, E; Ferreira, RAS; André, PSB

Groups

G1 - Porous Materials and Nanosystems
G2 - Photonic, Electronic and Magnetic Materials
G6 - Virtual Materials and Artificial Intelligence

Projects

CICECO - Aveiro Institute of Materials (UIDB/50011/2020)

CICECO - Aveiro Institute of Materials (UIDP/50011/2020)

Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)

acknowledgements

This work was financed by Portugal 2020 through European Regional Development Fund (ERDF) in the frame of CENTRO2020 in the scope of the projects CICECO-Aveiro Institute of Materials, UIDB/50011/2020 (https://doi.org/0.54499/UIDB/50011/2020), UIDP/50011/2020 (https://doi.org/10.54499/UIDB/50011/2020) and LA/P/0006/2020 (https://doi.org/10.54499/LA/P/0006/2020) and Instituto de Telecomunicacoes, UIDB/50008/2020 (https://doi.org/10.54499/UIDB/50008/2020), UIDP/50008/2020 (https://doi.org/10.54499/50008/2020) and LA/P/0109/2020 (https://doi.org/10.54499/LA/P/0109/2020) financed by national funds through the FCT/MEC (PIDDAC) and when appropriate co-financed by FEDER under the PT2020 Partnership through European Regional Development Fund (ERDF) in the frame of Operational Competitiveness and Internationalization Programme (POCI). LMSD thanks FCT for the PhD grant (https://doi.org/10.54499/UI/BD/153491/2022). RFPP acknowledges FCT for the Strategic Funding UID/00686: Centro de Quimica da Universidade do Minho (CQ-UM/UM) and FCT-UMinho for the Assistant Research contract (https://doi.org/10.54499/2023.07994.CEECIND/CP2841/CT0013). ARB acknowledges the financial support from IT-Lisbon and FCT under Contract No. CEECINST/00058/2021/CP2816/CT0004.