Analog Neural Network Model based on Logarithmic Four-Quadrant Multipliers
Abstract
Models for artificial intelligence, machine learning, and neural networks are implemented on
digital computers with a von Neumann architecture. Few studies have considered analog neural
networks. In our previous study, we used multipliers for representing connecting weights in a
neural network. The multipliers calculate the product of input signals and their corresponding
connecting weights. However, their operating range is limited by semiconductor characteristics.
The input and output ranges for networks that use these multipliers are thus limited. Furthermore,
the circuit operation becomes unstable. Here, we propose a logarithmic four-quadrant multiplier
for representing connecting weights. Experiments show that this multiplier exhibits stable operation
over a wide range. A model that uses only analog electronic circuits is presented. Its
learning time is quite short compared to that for models implemented on a digital computer. We
increased the number of units and network layers. We suggest the possibility of a hardware implementation
of a deep learning model.
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