Ferroelectric FET based Signed Synapses of Excitatory and Inhibitory Connection fo Stochastic Spiking Neural Network based Optimizer

Abstract

For combinatorial optimization problem (CSP) solving of spiking neural networks (SNNs), both excitatory and inhibitory synaptic connections are necessary for mapping of constraints, along with adaptively-stochastic neuron. In this work, for the first time, a novel ferroelectric FET (FeFET) based signed synapse with only two transistors is proposed and experimentally demonstrated to achieve excitatory and inhibitory connections, enabling cascade circuit with our previous proposed FeFET-based adaptively-stochastic neuron for all ferroelectric SNN optimizer. Based on the proposed design, a stochastic SNN is implemented for fast solving CSPs with accuracy improvement by 200%, providing a promising ultralow-hardware-cost and energy-efficient solution for optimization.