Synaptic weighting circuits for Cellular Neural Networks

Abstract

Cellular Neural Network (CNN) that can provide parallel processing in massive scale is known suitable to neuromorphic applications such as vision systems. In this paper, we propose a new synaptic weighting circuit that can perform analog multiplication for CNN applications. The common-mode feedback is used in the new weighting circuit to minimize the output offset. The multiplication accuracy can be degraded by finite High Resistance State (HRS) and non-zero Low Resistance State (LRS) of real memristors. To improve the multiplication accuracy, we added two MOSFET switches to the memristor weighting circuit and decided the weighting memristance very carefully considering the leakage current. Variations in memristance are analyzed to estimate how much they can affect the accuracy of analog multiplication. Finally, the Average and Laplacian template were tested and verified by the circuit simulation using the proposed weighting circuit.