Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators

The convergence of deep learning and big data has spurred significant interest in developing novel hardware that can run large artificial intelligence (AI) workloads more efficiently. Over the last several years, silicon photonics has emerged as a disruptive technology for next-generation accelerators for machine learning (ML). More recently, the heterogeneous integration of III-V compound semiconductors has opened the door to integrating lasers and semiconductor optical amplifiers at wafer-scale, enabling the scaling of the size, density, and complexity of silicon photonic integrated circuits (PICs). Furthermore, using this technology, all of the individual components required to execute the operations within a neural network are available and can be integrated on the same PIC. Here, we review our innovations of an energy-efficient and scalable silicon photonic platform serving as the underlying foundation for next-generation AI accelerator hardware.

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Publication Details

Journal
IEEE Journal of Selected Topics in Quantum Electronics
Published
2025-01-09
DOI
https://doi.org/10.1109/jstqe.2025.3527904
Citations
17
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
42.18
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article

Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators

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17 citations
IEEE Journal of Selected Topics in Quantum Electronics
Neural Networks and Reservoir Computing
42.18
article

Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators

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article en
17 citations

Abstract

The convergence of deep learning and big data has spurred significant interest in developing novel hardware that can run large artificial intelligence (AI) workloads more efficiently. Over the last several years, silicon photonics has emerged as a disruptive technology for next-generation accelerators for machine learning (ML). More recently, the heterogeneous integration of III-V compound semiconductors has opened the door to integrating lasers and semiconductor optical amplifiers at wafer-scale, enabling the scaling of the size, density, and complexity of silicon photonic integrated circuits (PICs). Furthermore, using this technology, all of the individual components required to execute the operations within a neural network are available and can be integrated on the same PIC. Here, we review our innovations of an energy-efficient and scalable silicon photonic platform serving as the underlying foundation for next-generation AI accelerator hardware.

IEEE Journal of Selected Topics in Quantum ElectronicsVol. 31(3: AI/ML Integrated Opto-)
Indian Institute of Technology Madras (IN), University of Michigan (US), Hewlett Packard Enterprise (United States) (US), Microsoft Research (United Kingdom) (GB)
Affordable and clean energy
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
42.18
DOIOpen Access PDFOpenAlex
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