Xingxing Guo
Papers
2
Total Citations
21
H-Index
2
About
Xingxing Guo is a rising leader in photonic neuromorphic computing, a field that harnesses light to create ultra-fast, energy-efficient artificial neural networks. Their research centers on developing novel hardware implementations for intelligent systems, with a particular focus on obstacle avoidance and multimodal recognition. Guo’s most notable contribution is the hardware implementation of an ultra-fast obstacle avoidance system using a single photonic spiking neuron, a breakthrough that addresses the critical payload and power constraints of mini UAVs. This work, published in 2023, has already garnered 19 citations, signaling its immediate impact on the robotics and autonomous systems community. More recently, Guo has advanced the field with a photonic neural network for multimodal recognition, leveraging a self-activated MAC function in DFB-SA lasers to dramatically improve energy efficiency and processing speed over traditional electronic processors. This 2025 paper, with 2 citations, represents a foundational step toward next-generation, light-based AI accelerators. Guo’s work sits at the exciting intersection of neuromorphic engineering, photonics, and robotics, promising to enable smarter, faster, and more power-efficient autonomous systems for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2