Details

Autor(en) / Beteiligte

• Xu, Lei
• de Lima, Thomas Ferreira
• Peng, Hsuan-Tung
• Bilodeau, Simon
• Tait, Alexander
• Shastri, Bhavin J.
• Prucnal, Paul R.

Titel

Scalable Networks of Neuromorphic Photonic Integrated Circuits

Ist Teil von

• IEEE journal of selected topics in quantum electronics, 2022, Vol.28 (6: High Density Integr. Multipur), p.1-10

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.