Bo Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Bo Xu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Renjing Xu
Microelectronics Thrust The authors are with The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China
1 shared publication - Shaoliang Yu
Zhejiang Laboratory Hangzhou China
1 shared publication - Yuetong Fang
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
1 shared publication - Yuhao Huang
Huazhong University of Science and Technology
1 shared publication - Zhongrui Wang
School of Microelectronics Southern University of Science and Technology Shenzhen China
1 shared publication
Latest Publications
- Open Access
Recent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture
The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.
Open AccessRecent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture
The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.