STADIA: Photonic Stochastic Gradient Descent for Neural Network Accelerators

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Title

STADIA: Photonic Stochastic Gradient Descent for Neural Network Accelerators

Content partner

University of Otago

Collection

Otago University Research Archive

Description

Deep Neural Networks (DNNs) have demonstrated great success in many fields such as image recognition and text analysis. However, the ever-increasing sizes of both DNN models and training datasets make deep leaning extremely computation- and memory-intensive. Recently, photonic computing has emerged as a promising technology for accelerating DNNs. While the design of photonic accelerators for DNN inference and forward propagation of DNN training has been widely investigated, the architectural ...

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Format

Research paper

Research format

Scholarly text / Journal article

Thesis level

Article

Date created

2023-09-09

Creator

Xia, Chengpeng / Chen, Yawen / Zhang, Haibo / Wu, Jigang

URL

https://hdl.handle.net/10523/24438

Related subjects

Computer systems organization / Emerging optical and photonic technologies / Hardware / Neural networks

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