Abstract
A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.
Highlights
Synaptic multiplications between input signals and weights are key operations in neural networks, programmable analog vector matrix multiplication and cellular neural networks
When M1, M4 and M2, M3 are in minimum and maximum state respectively, a negative wide voltage pulse is applied to the memristor bridge synapse, so that the memristance of memristor M1, M4 and M2, M3 are moved to the opposite direction compare to the positive case input pulse
This paper is the extension of our previous work on memristor bridge synapses [24]
Summary
Synaptic multiplications between input signals and weights are key operations in neural networks, programmable analog vector matrix multiplication and cellular neural networks. Sensors 2012, 12 accelerating board on which the software version of neural network is a practical option representing a compromise between limited flexibility and a high speed processing [5,6]. There have been some research efforts to build artificial synapses (weights) in neural network chip and analog programmable vector matrix multiplication using CMOS technologies [7,8,9,10,11]. In 2008, HP announced a successful fabrication of a very compact and non-volatile nano scale memory called the memristor [17] It was originally postulated by Chua [18,19] as the fourth basic circuit elements in electrical circuits. A simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits.
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