Physical Reservoir Computing

Physical reservoir computing (PRC) leverages the inherent nonlinear dynamics of physical systems to perform machine learning tasks, offering a potentially more energy-efficient alternative to traditional digital computing. Current research explores diverse physical substrates, including CMOS circuits, pneumatic actuators, quantum systems, and even soft biological tissues, often employing Echo State Networks or similar recurrent neural network architectures. This approach is significant for its potential to enable low-power, onboard AI in applications like robotics and autonomous systems, as well as providing new insights into the intersection of physics and computation.