A Hybrid-Integrated Photonic Spiking Neural Network Framework Based on an MZI Array and VCSELs-SA
Ziwei Song, Shuiying Xiang, Siting Zhao, Yahui Zhang, Xingxing Guo, Ye Tian, Yuechun Shi, Yue Hao
Abstract
We propose an inferencing framework of a hybrid-integrated photonic spiking neural network (PSNN) to perform pattern recognition tasks, where the linear computation is realized based on a 4 × 4 silicon photonic Mach-Zehnder interferometer (MZI) array, and the nonlinear computation is performed by an InP-based spiking neuron array based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA). With the modified Tempotron-like remote supervised method (ReSuMe) training algorithm, we realize two pattern recognition tasks, the recognition of numbers “0-3” and optical character recognition (OCR). The phase shifts in the MZI array are accurately configured to represent the weight matrix according to the decomposition procedure of a 4 × 4 triangular MZI mesh. Besides, the effects of the phase shift error and quantization precision of phase shifters (PSs) on the recognition performance are analyzed. For the OCR task, the 400 × 10 PSNN is realized by multiplexing the 4 × 4 MZI array based on the matrix blocking and the reconfigurability of the MZI array. This work provides a systematic computational model of the hybrid-integrated PSNN based on the silicon photonics and InP platforms, enabling the co-design and optimization of hardware architecture and algorithm, which contributes one step forward toward the construction of a hybrid-integrated PSNN hardware system.