Abstract
Neuromorphic photonic integrated circuits over silicon photonic platform have recently made significant progress. Photonic neural networks with a small number of neurons have demonstrated important applications in high-bandwidth, low latency machine learning (ML) type signal processing applications. Naturally an important topic is to investigate building a large scale photonic neural networks with high flexibility and scalability to potentially support ML type applications involving high-speed processing of a high volume of data. In this paper we revisited the architecture of microring resonator (MRR)-based non-spiking and spiking photonic neurons, and photonic neural networks using broadcast-and-weight scheme. We illustrate expanded neural network topologies by cascading photonic broadcast loops, to achieve scalable neural network scalability with a fixed number of wavelengths. Furthermore, we propose the adoption of wavelength selective switch (WSS) inside the broadcasting loop for wavelength-switched photonic neural network (WS-PNN). The WS-PNN architecture will find new applications of using off-chip WSS switches to interconnect groups of photonic neurons. The interconnection of WS-PNN can achieve unprecedented scalability of photonic neural networks while supporting a versatile selection of mixture of feedforward and recurrent neural network topologies.
Original language | English (US) |
---|---|
Article number | 6101409 |
Journal | IEEE Journal of Selected Topics in Quantum Electronics |
Volume | 28 |
Issue number | 6 |
DOIs | |
State | Published - 2022 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
Keywords
- Silicon photonic neural network
- neuromorphic photonic computing
- wavelength selective switching