An integrated photonics engine for unsupervised correlation detection

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge2Sb2Te5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Automated summary

With the digitization of modern life and scientific tools, the generation of data is growing exponentially, requiring fast and efficient statistical processing. This paper introduces a novel computational paradigm through the development of an integrated phase-change photonics engine. By exploiting the properties of phase-change cells and optics, the engine enables fully parallel and colocated temporal correlation detection computations. Experimental demonstrations on Twitter and data centers showcase the use of high-speed integrated photonics in accelerating statistical analysis methods.