Three dimensional INtegrated PhotonIcS to RevolutionizE deep Learning

ongoing

Neural networks revolutionize computation and already outperform humans according to many benchmarks. They have the potential to transform society and have already influenced Google to place artificial intelligence at the core (“AI first”) of business strategy. Governments and policy bodies worldwide focus on neural network-based computing as a vital future technology. Despite the evident promise, however, current architectures severely lose computational efficiency when applied to large neural networks and scale badly. The performance of current neural network hardware is orders of magnitude below what is theoretically possible, and the future development of artificial intelligence is therefore in jeopardy. I will solve this problem by developing neural network processors using advanced photonic components, specifically enabling a breakthrough with three-dimensional integrated photonic waveguides to implement a biologically inspired, fully parallel and scalable architecture. This is not an incremental improvement, but rather a completely new and never-explored approach, which will revolutionize neural network hardware. Based on this technology, I will create a fully programmable optical tensor processing unit by interfacing 3D waveguide Mach-Zehnder interferometers with a commercial SLM. The unit will calculate the connection of a neural network with speeds and energy efficiency exceeding the state of the art by two orders of magnitude, while programmability makes it widely applicable. A second system will go far beyond by implementing fully-fletched and programmable optical neural networks based on 3D waveguides and semiconductor lasers. The proof of principle building blocks of the project are in place and I am recognized as a leading researcher in the field. The realisation of this project will unlock doors to scalability, cascadability, and parallelism, and stimulate new research as well as applications in artificial intelligence.