Highly Reliable Inference System of Neural Networks Using Gated Schottky Diodes

Abstract

An inference system using gated Schottky diode (GSD) is proposed for highly reliable hardware-based neural networks (HNNs). We explain the characteristics of the GSD and present circuits that take into account the characteristics of the device. The reverse current of the GSD, which is the synaptic current, is saturated with respect to input voltage, which results in immunity of input and output noise and overcoming the IR drop problem in metal wire. In order to take advantages of this saturated I-V characteristics, pulse-width modulation (PWM) of input data instead of amplitude modulation is proposed. In addition, by applying identical pulses to the bottom gate, the synaptic current of the GSD increases linearly, which makes it easy to transfer the calculated weights to the conductance of GSDs. By considering these characteristics, electronic circuits for PWM, current sum, and activation function are designed. Through SPICE simulation, we evaluate the inference accuracy of a 2-layer neural network. The classification accuracy rate of 100 images of MNIST test sets is 94% accuracy obtained with software.

Keywords

• Neuromorphic
• synaptic device
• gated Schottky diodes
• hardware-based neural networks
• inference system