Weight bank addition photonic accelerator for artificial intelligence

Neural networks, a potent data-driven method for artificial intelligence, play a crucial role in modern estimation and classification tasks. However, they often encounter computational constraints. To overcome these, we propose photonics-based neuromorphic networks, offering faster processing than electronic systems. We focus on the weight bank, crucial for matrix multiplication, utilizing parallel cascaded micro-ring resonators (MRRs). Our study on silicon on insulators (SOI) demonstrates how cascaded MRRs address cross-talk issues in wavelength division multiplexing (WDM) systems. Additionally, we design a silicon photonic accelerator for weight addition, optimized for speed and energy efficiency, providing comparable performance to electronic devices.