from the can-you-hear-me-now? dept.
We are currently witnessing an explosion of network traffic. Numerous emerging services and applications, such as cloud services, video streaming platforms and the Internet of Things (IOT), are further increasing the demand for high-capacity communications. Optical communication systems, technologies that transfer information optically using fibers, are the backbone of today's communication networks of fixed-line, wireless infrastructure and data centers.
Over the past decade, the growth of the internet was enabled by a technique known as digital signal processing (DSP), which can help to reduce transmission distortions. However, DSP is currently implemented using CMOS integrated circuits (ICs), thus it relies heavily on Moore's Law, which has approached its limits in terms of power dissipation, density and feasible engineering solutions.
As a result, distortions caused by a phenomenon known as fiber nonlinearity cannot be compensated by DSP, as this would require too much computation power and resources. Fiber nonlinearities remain the major limiting effect on long-distance transmission systems.
Researchers at Princeton Lightwave Lab and NEC Laboratory America have recently created a new neural network hardware that could help to overcome this limitation, compensating for the adverse effects of fiber nonlinearity. This neural network, presented in a paper published in Nature Electronics, is run on a silicon-based photonic-electronic system composing of a few neurons, which can, in principle, outperform commercial DSP chips in throughput, latency and energy use."
"The research on 'neuromorphic photonics' at Princeton began with a discovery by our supervisor, Prof. Paul Prucnal, and neuroscientist David Rosenbluth," Chaoran Huang, one of the researchers who carried out the study, told Tech Xplore. "These two researchers found that photonic devices and biological neurons are governed by identical differential equations, yet 'photonic neurons' have a time scale of roughly picosecond to nanosecond whereas biological neurons have a time scale of roughly one millisecond."
Highly recommend reading the — long and detailed — linked article.
Chaoran Huang, Shinsuke Fujisawa, Thomas Ferreira de Lima, et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation, Nature Electronics (DOI: 10.1038/s41928-021-00661-2)
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing, (DOI: 10.1109/JLT.2014.2345652)