Scalable and Energy-Efficient Photonic Neural Networks Through Convolution Compression
Yixuan Li, Benshan Wang, Tengji Xu, Shaojie Liu, Qiarong Xiao, Chaoran Huang · 2024
This work experimentally demonstrates a convolution compression method to realize scalable and energy-efficient photonic neural networks. Our method achieves a 52-fold reduction in the component count and a 71.5-fold reduction in power consumption compared to conventional approaches, all while maintaining high computational accuracy.