A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals
Authors: 
Mingdai Yang, Mohammad Reza Jokar, Junyi Qiu, Qiuwen Lou, Yuming Liu, Aditi Udupa, Frederic T. Chong, John M. Dallesasse, Milton Feng, Lynford L. Goddard, 
X. Sharon Hu, Yanjing LiAuthors Info & Claims
Mingdai Yang, Mohammad Reza Jokar, Junyi Qiu, Qiuwen Lou, Yuming Liu, Aditi Udupa, Frederic T. Chong, John M. Dallesasse, Milton Feng, Lynford L. Goddard, X. Sharon Hu, Yanjing LiAuthors Info & ClaimsPages 271 - 276
Abstract
We present a hybrid optical-electrical analog deep learning (DL) accelerator, the first work to use incoherent optical signals for DL workloads. Incoherent optical designs are more attractive than coherent ones as the former can be more easily realized in practice. However, a significant challenge in analog DL accelerators, where multiply-accumulate operations are dominant, is that there is no known solution to perform accumulation using incoherent optical signals. We overcome this challenge by devising a hybrid approach: accumulation is done in the electrical domain, while multiplication is performed in the optical domain. The key technology enabler of our design is the transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently to tightly integrate electrical and optical devices into compact circuits. As such, our design fully realizes the ultra high-speed and high-energy-efficiency advantages of analog and optical computing. Our evaluation results using the MNIST benchmark show that our design achieves 2214× and 65× improvements in latency and energy, respectively, compared to a state-of-the-art memristor-based analog design.
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- A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals
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Published In
June 2021
504 pages
ISBN:9781450383936
DOI:10.1145/3453688
- General Chairs:
- Yiran Chen,
- Victor Zhirnov,
- Program Chairs:
- Avesta Sasan,
- Ioannis Savidis
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Published: 22 June 2021
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- E2CDA-NRI
- NSF (National Science Foundation)
- NSF 1405959
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