Ram Kaji Budhathoki

A Circuit-Based Learning Architecture for Multilayer Neural Networks With Memristor Bridge Synapses

Memristor-based circuit architecture for multilayer neural networks is proposed. It is a first of its kind demonstrating successful circuit-based learning for multilayer neural network built with memristors. Though back-propagation algorithm is a powerful learning scheme for multilayer neural networks, its hardware implementation is very difficult due to complexities of the neural synapses and the operations involved in the learning algorithm. In this paper, the circuit of a multilayer neural network is designed with memristor bridge synapses and the learning is realized with a simple learning algorithm called Random Weight Change (RWC). Though RWC algorithm requires more iterations than back-propagation algorithm, we show that a circuit-based learning using RWC is two orders faster than its software counterpart. The method to build a multilayer neural network using memristor bridge synapses and a circuit-based learning architecture of RWC algorithm is proposed. Comparison between software-based and memristor circuit-based learning are presented via simulations.

Composite Behavior of Multiple Memristor Circuits

Composite characteristics of the parallel and serial connections of memristors are investigated. The memristor is one of the fundamental electrical elements, which has recently been successfully built. However, its electrical characteristics are not yet fully understood. When multiple memristors are connected to each other, the composite behavior of the devices becomes complicated and is difficult to predict, due to the polarity dependent nonlinear variation in the memristance of individual memristors. In this work, we investigate the relationships among flux, charge, and memristance of diverse composite memristors, using both linear and nonlinear memristor models, and analyze the characteristics of complex memristor circuits.

Transient Behaviors of Multiple Memristor Circuits Based on Flux Charge Relationship

Memristor, a new electrical element, can have various configurations of multiple memristors, including serial and parallel connections like previous elements R, L and C. When input voltage/current is supplied to a circuit with multiple memristors, the composite behavior of the memristor circuit exhibits transient states before it enters a steady state. During the transient state period, the behavior is very complex and not predictable due to each memristors different action depending upon its connection polarity and initial state. In this paper, the transient characteristics of a composite memristor are analyzed via the relationships of charge, flux and memristance of each memristor. Also, the behavior of an individual memristor is formulated mathematically and a general computation method of composite memristance for multiple-memristor circuits of diverse configurations is proposed. Various simulations have also been performed to verify the effectiveness of the proposed method for differently configured memristor circuits, in terms of polarities and initial states.

Mutator-Based Meminductor Emulator for Circuit Applications

A mutator-based meminductor emulator whose inductance can be varied by an external current source is proposed. The implementation of a meminductor emulator is very important, since real meminductors are not physically realizable with current technology. Though there is active research on memristor or memcapacitor emulators, no significant contribution for the meminductor emulator has been presented yet. In this paper, a meminductor emulator has been built using the principle of a mutator, in that a memristor with

Features of memristor emulator-based artificial neural synapses

v-i

Composite memristance of parallel and serial memristor circuits

v-i at one port functions as a meminductor in the other port, whose input is characterized by

A mutator-based meminductor emulator circuit

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