부산대학교 도서관

Enabled by the availability of both excitatory and inhibitory post-synaptic currents, a biological neural network is inherently capable of implementing more sophisticated non-monotonic classification schemes. While such currents are readily emulated in a software-based artificial neural network using both positive and negative synaptic weighting factors, the same is not as straight forward in a hardware implementation. In this work, two dual-gate thin-film transistors with effectively infinite direct-current input impedance are deployed to construct the excitatory and inhibitory conduction channels of an artificial synapse. The utility of a hardware-based artificial neural network constructed using such synapses is demonstrated by its deployment in the implementation of the complete set of sixteen 2-input binary logic functions exhibiting both monotonic and non-monotonic behavior. The set of weighting factors needed for the implementation of each function are determined using a neuromorphic feed-forward training algorithm based on gradient-descent. While some of the functions can be implemented using the simplest reconfigurable $\mathbf {2\times 1}$ network, all 16 of the functions can be implemented using a deeper, reconfigurable $\mathbf {3\times 2\times 1}$ network.