High-speed mobile networks, connected devices, and widescale adoption of AI technologies contribute to an exponential generation of data that needs to be processed rapidly and efficiently. With conventional chips often found wanting, an international group of scientists has collaborated to present a photonic hardware accelerator that is capable of operating at speeds of “trillions of multiply-accumulate operations per second,” far beyond the capabilities of existing computer processors. The breakthrough study, published in Nature, was carried out by scientists from EPFL, the Universities of Oxford, Münster, Exeter, Pittsburgh, and IBM Research – Zurich.
The scientists have adopted a new approach and architecture that integrates processing and data storage onto a single chip by using light-based or “photonic” processors that outperform conventional electronic chips by achieving “parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs.” Professor Tobias Kippenberg, EcoCloud member and one of the authors of the study, elaborates on the use of a chip-based “frequency comb” (a technology developed at EPFL) as a light source: “Our study is the first to apply frequency combs in the field of artificial neural networks. The frequency comb provides a variety of optical wavelengths that are processed independently of one another in the same photonic chip.”
Speaking about the advantage of using light-based processors, co-author Wolfram Pernice (Münster University) said that it is “much faster than conventional chips which rely on electronic data transfer, such as graphic cards or specialized hardware like TPU’s.”
The photonic chip developed by the researchers was tested on a neural network that recognizes hand-written numbers. According to the authors, the results of their study indicate the applicability of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.
The study was funded by EPSRC, Deutsche Forschungsgemeinschaft (DFG), European Research Council, European Union’s Horizon 2020 Research and Innovation Programme (Fun-COMP), and Studienstiftung des deutschen Volkes.