Photonic and optoelectronic neuromorphic computing

Recent advances in neuromorphic computing have established a computational framework that removes the processor-memory bottleneck evident in traditional von Neumann computing. Moreover, contemporary photonic circuits have addressed the limitations of electrical computational platforms to offer energy-efficient and parallel interconnects independently of the distance. When employed as synaptic interconnects with reconfigurable photonic elements, they can offer an analog platform capable of arbitrary linear matrix operations, including multiply-accumulate operation and convolution at extremely high speed and energy efficiency. Both all-optical and optoelectronic nonlinear transfer functions have been investigated for realizing neurons with photonic signals. A number of research efforts have reported orders of magnitude improvements estimated for computational throughput and energy efficiency. Compared to biological neural systems, achieving high scalability and density is challenging for such photonic neuromorphic systems. Recently developed tensor-train-decomposition methods and three-dimensional photonic integration technologies can potentially address both algorithmic and architectural scalability. This tutorial covers architectures, technologies, learning algorithms, and benchmarking for photonic and optoelectronic neuromorphic computers. (C) 2022 Author(s).

Automated summary

Recent advances in neuromorphic computing have overcome the bottleneck of traditional computing and introduced energy-efficient and parallel interconnects through photonic circuits. These circuits enable high-speed and energy-efficient linear matrix operations when combined with reconfigurable photonic elements. Nonlinear transfer functions have been explored for implementing neurons with photonic signals. Scalability and density remain challenges for photonic neuromorphic systems, but tensor-train-decomposition methods and three-dimensional photonic integration technologies show promise in addressing these issues.