Hardware optimization for photonic time-delay reservoir computer dynamics

Abstract

Reservoir computing (RC) is one kind of neuromorphic computing mainly applied to process sequential data such as time-dependent signals. In this paper, the bifurcation diagram of a photonic time-delay RC system is thoroughly studied, and a method of bifurcation dynamics guided hardware hyperparameter optimization is presented. The time-evolution equation expressed by the photonic hardware parameters is established while the intrinsic dynamics of the photonic RC system is quantitively studied. Bifurcation dynamics based hyperparameter optimization offers a simple yet effective approach in hardware setting optimization that aims to reduce the complexity and time in hardware adjustment. Three benchmark tasks, nonlinear channel equalization (NCE), nonlinear auto regressive moving average with 10th order time lag (NARMA10) and Santa Fe laser time-series prediction tasks are implemented on the photonic delay-line RC using bifurcation dynamics guided hardware optimization. The experimental results of these benchmark tasks achieved overall good agreement with the simulated bifurcation dynamics modeling results.