Shiyin Du

Open AccessArticle by Zilong Tao 1, Hao Ouyang 2, Qiuquan Yan 1, Shiyin Du 1, Hao Hao 2,*, Jun Zhang 3 and Jie You 3,* 1 College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China 2 Institute for Quantum Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, China 3 National Innovation Institute of Defense Technology, Academy of Military Sciences PLA China, Beijing 100071, China * Authors to...

The explosive growth in computational demands of artificial neural networks has spurred research into optical neural networks. However, most existing work overlooks the co-design of software and hardware, resulting in challenges with data encoding and nonlinear activation in optical neural networks, failing to fully leverage the potential of optical computing hardware.

Nonlinear activation functions (NAFs) are essential in artificial neural networks, enhancing learning capabilities by capturing complex input–output relationships. However, most NAF implementations rely on additional optoelectronic devices or digital computers, reducing the benefits of optical computing.