Simulation platform for pattern recognition based on reservoir computing with memristor networks
This work addresses the problem of designing energy-efficient machine learning hardware for researchers and engineers, though it is incremental as it builds on existing reservoir computing methods with memristors.
The authors tackled the challenge of optimizing memristor-based reservoir computing systems for pattern recognition by developing a simulation platform to test different designs, achieving computational performance comparable to state-of-the-art methods in three time series classification tasks.
Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward a realization of energy-efficient machine learning hardware.