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Meta-optics for spatial optical analog computing [PDF]

Nanophotonics, 2020
Rapidly growing demands for high-performance computing, powerful data processing, and big data necessitate the advent of novel optical devices to perform demanding computing processes effectively.
Abdollahramezani Sajjad, Hemmatyar Omid, Adibi Ali   +2 more
doaj   +5 more sources

Analog Optical Computing for Artificial Intelligence

Engineering, 2022
The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.
Jiamin Wu, Xing Lin, Yuchen Guo, Junwei Liu, Lu Fang, Shuming Jiao, Qionghai Dai   +6 more
doaj   +5 more sources

Optical Realization of Wave-Based Analog Computing with Metamaterials [PDF]

Applied Sciences, 2020
Recently, the study of analog optical computing raised renewed interest due to its natural advantages of parallel, high speed and low energy consumption over conventional digital counterpart, particularly in applications of big data and high-throughput ...
Kaiyang Cheng, Yuancheng Fan, Weixuan Zhang, Yubin Gong, Shen Fei, Hongqiang Li   +5 more
doaj   +2 more sources

Digital-analog hybrid matrix multiplication processor for optical neural networks [PDF]

Nature Communications
Optical neural networks (ONNs) promise computing efficiency beyond microelectronics for modern artificial intelligence (AI). Current ONNs using analog matrix-vector multiplication (MVM) implementations are fundamentally limited in numerical precision due
Xiansong Meng, Deming Kong, Kwangwoong Kim, Qiuchi Li, Po Dong, Ingemar J. Cox, Christina Lioma, Hao Hu   +7 more
doaj   +2 more sources

Neuromorphic photonic computing with an electro-optic analog memory. [PDF]

Nat Commun
In neuromorphic photonic systems, device operations are typically governed by analog signals, necessitating digital-to-analog converters (DAC) and analog-to-digital converters (ADC). However, data movement between memory and these converters in conventional von Neumann architectures incur significant energy costs. We propose an analog electronic memory
Lam S, Khaled A, Bilodeau S, Marquez BA, Prucnal PR, Chrostowski L, Shastri BJ, Shekhar S.   +7 more
europepmc   +4 more sources

Analog readout for optical reservoir computers

, 2012
Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital ...
Duport, François, Haelterman, Marc, Massar, Serge, Paquot, Yvan, Schrauwen, Benjamin, Smerieri, Anteo   +5 more
core   +2 more sources

Microcomb-enabled parallel self- calibration optical convolution streaming processor [PDF]

Light: Science & Applications
The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth, energy-efficient hardware architectures in data centers.
Jiajia Wang, Xingyuan Xu, Xiaotian Zhu, Yifu Xu, Shifan Chen, Haoran Zhang, Yixuan Zheng, Shuying Li, Yunping Bai, Zhihui Liu, Roberto Morandotti, Brent E. Little, Sai T. Chu, Arthur J. Lowery, David J. Moss, Kun Xu   +15 more
doaj   +2 more sources

Photonic edge intelligence chip for multi-modal sensing, inference and learning [PDF]

Nature Communications
Edge computing requires real-time processing of high-throughput analog signals, posing a major challenge to conventional electronics. Although integrated photonics offers low-latency processing, it struggles to directly handle raw analog data.
Shiji Zhang, Xueyi Jiang, Bo Wu, Haojun Zhou, Wenguang Xu, Hailong Zhou, Zhichao Ruan, Jianji Dong, Xinliang Zhang   +8 more
doaj   +2 more sources

Double-phase metasurface operators for all-optical image processing [PDF]

Light: Science & Applications
All-optical image processing offers a high-speed, energy-efficient alternative to conventional electronic systems by leveraging the wave nature of light for parallel computation.
Linzhi Yu, Haobijam J. Singh, Jesse Pietila, Humeyra Caglayan   +3 more
doaj   +2 more sources

Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing [PDF]

Nature Communications
Inverse-designed nanophotonic devices offer promising solutions for analog optical computation, where high-density photonic integration is critical for scaling computational complexity.
Joel Sved, Shijie Song, Liwei Li, George Li, Debin Meng, Xiaoke Yi   +5 more
doaj   +2 more sources