Large-scale and energy-efficient tensorized optical neural networks on III–V-on-silicon MOSCAP platform
Xian Xiao, Mehmet Berkay On, Thomas Van Vaerenbergh, Di Liang, Raymond G. Beausoleil, S. J. Ben Yoo
Abstract
This paper proposes a large-scale, energy-efficient, high-throughput, and compact tensorized optical neural network (TONN) exploiting the tensor-train decomposition architecture on an integrated III–V-on-silicon metal–oxide–semiconductor capacitor (MOSCAP) platform. The proposed TONN architecture is scalable to 1024 × 1024 synapses and beyond, which is extremely difficult for conventional integrated ONN architectures by using cascaded multi-wavelength small-radix (e.g., 8 × 8) tensor cores. Simulation experiments show that the proposed TONN uses 79× fewer Mach–Zehnder interferometers (MZIs) and 5.2× fewer cascaded stages of MZIs compared with the conventional ONN while maintaining a >95% training accuracy for Modified National Institute of Standards and Technology handwritten digit classification tasks. Furthermore, with the proven heterogeneous III–V-on-silicon MOSCAP platform, our proposed TONN can improve the footprint-energy efficiency by a factor of 1.4 × 104 compared with digital electronics artificial neural network (ANN) hardware and a factor of 2.9 × 102 compared with silicon photonic and phase-change material technologies. Thus, this paper points out the road map of implementing large-scale ONNs with a similar number of synapses and superior energy efficiency compared to electronic ANNs.