2021 Vol. 4, No. 11

Cover Story: Liao K, Chen Y, Yu ZC, Hu XY, Wang XY et al. All-optical computing based on convolutional neural networks. Opto-Electron Adv 4, 200060 (2021).

Traditional electronic processor, the pre-dominant computing platform to date, suffers from speed and energy consumption limitations under von Neumann architecture. All-optical computing adopting photons as information carriers offers a promising alternative approach. However, ultrafast response and giant nonlinearity often presents an inherent trade-off in optical materials, mandating complicated heterogeneous integration of various photonic devices in a single chip following von Neumann architecture. Therefore, exploring new architecture and unconventional computing scheme for all-optical computing becomes imperative. Recently, the research group of Prof. Xiaoyong Hu and Prof. Qihuang Gong from School of physics, Peking University, proposes a new strategy to realize ultrafast and ultralow-energy-consumption all-optical computing chip based on convolutional neural network (CNN), which supports the execution of multiple computing tasks. The optical CNN consists of cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments designed to control the amplitude and phase of light in the waveguide branches. As a proof-of-concept, they experimentally implemented the network design through several computation tasks including transcendental equations solvers, multifunctional logic gate operators, and half-adders. The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit. Their approach can be further expanded to offer the possibility of parallel computing using wavelength multiplexing based on non-von Neumann architecture and thus paves a new way for on-chip all-optical computing.


2022 Vol. 5, No. 11

ISSN 2096-4579
CN 51-1781/TN
Prof. Xiangang Luo
Executive Editor-in-Chief: