Abstract
This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are called memristor programming NNs (MPNNs), use a set of filamentary-type memristors with sharp memristance transitions for constraint satisfaction and an additional set of memristors with smooth memristance transitions for memorizing the result of a computation. The nonlinear dynamics and global optimization capabilities of MPNNs for QP and LP problems are thoroughly investigated via a recently introduced technique called the flux-charge analysis method. One main feature of MPNNs is that the processing is performed in the flux-charge domain rather than in the conventional voltage-current domain. This enables exploiting the unconventional features of memristors to obtain advantages over the traditional NNs for QP and LP problems operating in the voltage-current domain. One advantage is that operating in the flux-charge domain allows for reduced power consumption, since in an MPNN, voltages, currents, and, hence, power vanish when the quick analog transient is over. Moreover, an MPNN works in accordance with the fundamental principle of in-memory computing, that is, the nonlinearity of the memristor is used in the dynamic computation, but the same memristor is also used to memorize in a nonvolatile way the result of a computation.
Highlights
C URRENT data-intensive applications in the Internet of Things (IoT), cloud computing, and edge computing call for ever-increasing data processing capabilities and the availability of computing devices with lower power consumption [1]–[3]
One main novelty of the proposed memristor programming NNs (MPNNs) is that the analog computation and optimization is performed in the flux–charge domain rather than in the traditional voltage–current domain as it happens for neural networks (NNs) introduced so far to solve quadratic programming (QP) and linear programming (LP) problems
To the best of our knowledge, the MPNNs introduced in this article are the first NNs using the unconventional features of memristors to obtain advantages for solving QP and LP problems with respect to traditional NNs
Summary
C URRENT data-intensive applications in the Internet of Things (IoT), cloud computing, and edge computing call for ever-increasing data processing capabilities and the availability of computing devices with lower power consumption [1]–[3]. 3) Examples and numerical simulations are provided to verify and illustrate the effectiveness, advantages, and optimization capabilities of MPNNs. One main novelty of the proposed MPNNs is that the analog computation and optimization is performed in the flux–charge domain rather than in the traditional voltage–current domain as it happens for NNs introduced so far to solve QP and LP problems. The role of memristors is two-fold: their nonlinearity is used for analog computation purposes, memristors are used to store in a nonvolatile way the result of computation This is basically different from NNs computing in the voltage–current domain, where the result of computation is the asymptotic value of the voltage on capacitors.
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