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Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators
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AI
DOI：10.1109/JSTQE.2025.3527904.png)
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Abstract
En 中文
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.
Keywords:
Silicon photonics
optical computing
optical computing
neuromorphic computing
heterogeneous integration
heterogeneous integration
memristors
memristors
Journal
I
IF:
5.1
Papers:
5.6K
Citations:
1.2W
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