Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch

We propose a new method for performing photonic circuit simulations based on the scatter matrix formalism. We leverage the popular deep-learning framework PyTorch to reimagine photonic circuits as sparsely connected complex-valued neural networks. This allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation

Photontorch : simulation and optimization of large photonic circuits using the deep learning framework PyTorch

We propose a new framework for simulating and optimizing large photonic circuits. The framework utilizes gpu-acceleration to efficiently simulate the circuits in a highly parallel manner, while optimization is achieved through backpropagation by essentially viewing the network as a sparsely connected recurrent neural network

Silicon photonics for neuromorphic information processing

We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power efficiency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells

Photonic neuromorphic information processing and reservoir computing

Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing