@article{Zhao2025, 
author = {Ding Zhao and Jie Bao},
title = {Quantum dot fluorescence-based dynamic-static matrix multiplication photonic acceleration architecture},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {9},
pages = {94907957},
keywords = {quantum dot, optical computing, photonic accelerator, dynamic-static matrix multiplication},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907957},
doi = {10.26599/NR.2025.94907957},
abstract = {Optical computing accelerators, with high parallelism, large bandwidth, and low transmission loss, have the potential to enhance electronic computing in both computational power and energy efficiency. Photonic acceleration plays a crucial role in supporting computationally intensive operations, such as dynamic-static matrix multiplication, significantly improving overall efficiency. Existing photonic architectures for dynamic-static matrix multiplication depend on complex coherent optical systems or costly nano-optics fabrication, limiting scalability. This study introduces a novel quantum dot fluorescence-based dynamic-static matrix multiplication photonic acceleration architecture that eliminates the need for coherent light sources or intricate fabrication. By leveraging simple, cost-effective quantum dot preparation and printing techniques, this architecture has significant potential for large-scale, high-performance, low-cost photonic accelerators. We detail the mathematical and physical mechanisms of the proposed architecture, experimentally validate the key physical processes, and demonstrate its application in template matching for image recognition, achieving 95% accuracy.}
}