Related references
Note: Only part of the references are listed.
Article
Engineering, Electrical & Electronic
Xingyuan Xu et al.
Summary: Optical neural networks (ONNs) based on wavelength-division multiplexing (WDM) techniques offer high bandwidth and analog architecture for enhanced computing power and energy efficiency. Integrated microcombs have been used to implement ONNs, with successful applications such as optical convolution accelerators for human image processing at 11 Tera operations per second. However, challenges and limitations of ONNs still need to be addressed for future applications.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2023)
Review
Optics
Hailong Zhou et al.
Summary: Photonic matrix multiplication is a crucial part of information processing in science and technology, offering faster and more efficient computations in the optical domain. Recent research has shown its potential for applications in telecommunications and artificial intelligence that are beyond the capabilities of conventional processors.
LIGHT-SCIENCE & APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
H. H. Zhu et al.
Summary: Large-scale, highly integrated, and low-power-consuming hardware is crucial for optical neural networks to achieve advanced optical computing. Traditional experimental implementations have limited scalability and consume excessive power. This study proposes an integrated diffractive optical network (IDNN) that utilizes two ultracompact diffractive cells and a small number of MZIs to perform parallel Fourier transforms, convolution operations, and application-specific optical computing. The footprint and energy consumption of the IDNN scale linearly with the input data dimension, resulting in significant reductions compared to traditional MZI-based ONNs. Experimental results on MNIST and Fashion-MNIST datasets demonstrate a similar level of accuracy and approximately 10-fold reduction in footprint and energy consumption. The IDNN chip shows promising potential for scalable and low-power-consumption optical computational chips for optical artificial intelligence.
NATURE COMMUNICATIONS
(2022)
Article
Optics
Weipeng Zhang et al.
Summary: This study experimentally demonstrates a record-high precision for photonic synapses with a dithering control scheme and numerically investigates the impact of increased synaptic precision on signal classification. It provides insights into the potential of photonic neural networks for practical applications.
Article
Multidisciplinary Sciences
Xingyuan Xu et al.
Summary: Inspired by biological visual cortex systems, convolutional neural networks extract hierarchical features of raw data, reducing parameter complexity and improving prediction accuracy. Optical neural networks promise faster computing using broad optical bandwidths, with optical vector convolutional accelerators demonstrating efficient image processing capabilities.
Review
Computer Science, Information Systems
Dulari Bhatt et al.
Summary: Computer vision is increasingly popular in image processing, with deep CNN benefiting various fields such as video processing, object recognition, and natural language processing. Researchers have explored innovative concepts and architectural advancements to enhance CNN performance and capacity, focusing on leveraging channel and spatial information for information processing.
Article
Multidisciplinary Sciences
John Shalf
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2020)
Review
Multidisciplinary Sciences
Gordon Wetzstein et al.
Summary: Artificial intelligence tasks require accelerators for fast and low-power execution. While general-purpose optical computing systems have not matured into a practical technology, recent research shows promise for optical computing in artificial intelligence applications, particularly for visual computing. The potential and challenges of all-optical and hybrid optical networks are discussed in this Perspective.
Review
Computer Science, Information Systems
Maurizio Capra et al.
Article
Computer Science, Artificial Intelligence
Jiuxiang Gu et al.
PATTERN RECOGNITION
(2018)
Review
Engineering, Electrical & Electronic
Sujeet Patole et al.
IEEE SIGNAL PROCESSING MAGAZINE
(2017)
Article
Optics
Satoshi Shimizu et al.
Article
Optics
D. Hillerkuss et al.
Article
Optics
Zhenxing Wang et al.
Article
Optics
D. Hillerkuss et al.