Abstract

We recently implemented in hardware input as well as readout weights and a recurrent nonlinear neural network with 10,000 neurons by leveraging the high-dimensional state space of a multi-mode semiconductor laser. For maximal efficiency, the largest fraction of a NN’s hardware should be dedicated to the core computational task, while auxiliary infrastructure should be pushed into the background. I will demonstrate in situ training of our autonomous photonic NN with minimal support by a classical digital computer. We achieve this by employing exclusively black-box, evolutionary optimization algorithms, and I will show that for real-world analog neural networks these hold substantial promises while removing the most critical block to truly hardware based realtime learning.