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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 …
American Association for the Advancement of Science
Publication date: 
3 Jun 2022

Syed Ghazi Sarwat, Frank Brückerhoff-Plückelmann, Santiago García-Cuevas Carrillo, Emanuele Gemo, Johannes Feldmann, Harish Bhaskaran, C David Wright, Wolfram HP Pernice, Abu Sebastian

Biblio References: 
Volume: 8 Issue: 22 Pages: eabn3243
Science Advances