Simulation platform for pattern recognition based on reservoir computing
with memristor networks
- URL: http://arxiv.org/abs/2112.00248v2
- Date: Sun, 19 Jun 2022 00:45:33 GMT
- Title: Simulation platform for pattern recognition based on reservoir computing
with memristor networks
- Authors: Gouhei Tanaka and Ryosho Nakane
- Abstract summary: We develop a simulation platform for reservoir computing (RC) with memristor device networks.
We 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.
- Score: 1.5664378826358722
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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