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Time-space-wavelength multiplexed photonic tensor core using WDM SiGe EAM array chiplets.

A Prapas, M Moralis-Pegios, A Tsakyridis

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    Summary
    This summary is machine-generated.

    Photonic neural networks (PNNs) offer energy-efficient AI acceleration. This study demonstrates an 8x8 photonic integrated tensor core (PITC) architecture for AI workloads, achieving high classification accuracy on benchmark datasets.

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    Area of Science:

    • Photonics
    • Artificial Intelligence
    • Integrated Circuits

    Background:

    • Photonic neural networks (PNNs) are emerging as a next-generation AI platform.
    • They offer significant advances in compute energy and area efficiency by utilizing light's parallelism.
    • Leveraging light across space, wavelength, and time is key to PNNs' potential.

    Purpose of the Study:

    • To present a novel photonic integrated tensor core (PITC) architecture.
    • To validate the PITC architecture experimentally for AI benchmark workloads.
    • To demonstrate multi-wavelength data and weight encoding in a chiplet-based silicon photonic setup.

    Main Methods:

    • Developed an 8x8 PITC architecture combining an AWGR-enabled PNN with a SiGe EAM array.
    • Utilized a chiplet-based silicon photonic accelerator setup.
    • Experimentally validated the architecture using AI benchmark workloads at 20 Gbaud.

    Main Results:

    • Successfully classified IRIS and MNIST datasets using the PITC architecture.
    • Achieved Cohen's kappa-scores of 0.8438 for IRIS and 0.7421 for MNIST.
    • Demonstrated the feasibility of the proposed PITC architecture for AI applications.

    Conclusions:

    • The PITC architecture shows promise for efficient AI acceleration.
    • Experimental validation confirms the potential of integrated photonics for AI.
    • This work paves the way for advanced photonic AI hardware.