Scalable Networks of Neuromorphic Photonic Integrated Circuits
IEEE Journal of Selected Topics in Quantum Electronics(2022)
摘要
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.
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关键词
Silicon photonic neural network,neuromorphic photonic computing,wavelength selective switching
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