Single Germanium MOSFET-Based Low Energy and Controllable Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Mudasir A. Khanday, Faisal Bashir, Farooq Ahmad Khanday
Abstract
In this work, a single transistor based on germanium (Ge) is used to construct a leaky integrate-and-fire (LIF) neuron with significant improvement in energy efficiency, area efficiency, and reduction in cost. Using 2-D calibrated simulation, we validated that Ge-MOSFET LIF neuron is able to imitate the neuron behavior accurately. The Ge-MOSFET shows low breakdown voltage, high impact ionization coefficient, and sharp breakdown. All these factors are responsible for achieving low energy per spike and higher spiking current. The proposed Ge-MOSFET-based spiking LIF neuron needs only 8 pJ/spike of energy as compared to recently reported silicon-based silicon-on-insulator (SOI) MOSFET, which needs 45 pJ/spike of energy. The use of gate voltage makes Ge-MOSFET LIF neuron firing controllable, which can improve the energy efficiency of the spiking neural network (SNN) by inducing sparse action.