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

被引:0
|
作者
Tossoun, Bassem [1 ]
Xiao, Xian [1 ]
Cheung, Stanley [1 ]
Yuan, Yuan [1 ]
Peng, Yiwei [1 ]
Srinivasan, Sudharsanan [2 ]
Giamougiannis, George [3 ]
Huang, Zhihong [1 ]
Singaraju, Prerana [1 ]
London, Yanir [1 ]
Hejda, Matej [1 ]
Sundararajan, Sri Priya [1 ]
Hu, Yingtao [1 ]
Gong, Zheng [1 ]
Baek, Jongseo [1 ]
Descos, Antoine [1 ]
Kapusta, Morten [1 ]
Bohm, Fabian [1 ]
Van Vaerenbergh, Thomas [1 ]
Fiorentino, Marco [1 ]
Kurczveil, Geza [1 ]
Liang, Di [4 ]
Beausoleil, Raymond G. [1 ]
机构
[1] Hewlett Packard Enterprise, Hewlett Packard Labs, Milpitas, CA 95305 USA
[2] Indian Inst Technol Madras, Dept Elect Engn, Chennai 600036, India
[3] Microsoft Res, Cambridge CB1 2FB, England
[4] Univ Michigan, Dept Elect & Comp Engn, Ann Arbor, MI 30332 USA
关键词
Silicon photonics; optical computing; neuromorphic computing; heterogeneous integration; memristors; CONTENT-ADDRESSABLE MEMORY; AVALANCHE PHOTODIODES; MICRORING LASER; NEURAL-NETWORKS; HYBRID; DESIGN;
D O I
10.1109/JSTQE.2025.3527904
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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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页数:26
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