Neural networks powered by artificial intelligence play a pivotal role in
current estimation and classification applications due to the escalating
computational demands of evolving deep learning systems. The hindrances posed
by existing computational limitations threaten to impede the further
progression of these neural networks. In response to these issues, we propose
neuromorphic networks founded on photonics that offer superior processing speed
than electronic counterparts, thereby enhancing support for real time, three
dimensional, and virtual reality applications. The weight bank, an integral
component of these networks has a direct bearing on their overall performance.
Our study demonstrates the implementation of a weight bank utilizing parallelly
cascaded micro ring resonators. We present our observations on neuromorphic
networks based on silicon on insulators, where cascaded MRRs play a crucial
role in mitigating interchannel and intrachannel cross talk, a persistent issue
in wavelength division multiplexing systems. Additionally, we design a standard
silicon photonic accelerator to perform weight addition. Optimized to offer
increased speed and reduced energy consumption, this photonic accelerator
ensures comparable processing power to electronic devices