Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing
This addresses a critical limitation for researchers and engineers working with physical reservoir computing, enabling integration into deeper neural networks for potential energy efficiency gains, though it appears incremental as it adapts existing perturbation ideas to this specific bottleneck.
The paper tackles the problem of training physical reservoir computing systems where backpropagation is impossible due to their black-box nature, by introducing Perturbative Gradient Training (PGT) which uses random perturbations to approximate gradients with forward passes only, achieving performance comparable to standard backpropagation in simulated and experimental setups.
We introduce Perturbative Gradient Training (PGT), a novel training paradigm that overcomes a critical limitation of physical reservoir computing: the inability to perform backpropagation due to the black-box nature of physical reservoirs. Drawing inspiration from perturbation theory in physics, PGT uses random perturbations in the network's parameter space to approximate gradient updates using only forward passes. We demonstrate the feasibility of this approach on both simulated neural network architectures, including a dense network and a transformer model with a reservoir layer, and on experimental hardware using a magnonic auto-oscillation ring as the physical reservoir. Our results show that PGT can achieve performance comparable to that of standard backpropagation methods in cases where backpropagation is impractical or impossible. PGT represents a promising step toward integrating physical reservoirs into deeper neural network architectures and achieving significant energy efficiency gains in AI training.