  Current advances in emerging memory technologies enable novel and
unconventional computing architectures for high-performance and low-power
electronic systems, capable of carrying out massively parallel operations at
the edge. One emerging technology, ReRAM, also known to belong in the family of
memristors (memory resistors), is gathering attention due to its attractive
features for logic and in-memory computing; benefits which follow from its
technological attributes, such as nanoscale dimensions, low power operation and
multi-state programming. At the same time, design with CMOS is quickly reaching
its physical and functional limitations, and further research towards novel
logic families, such as Threshold Logic Gates (TLGs) is scoped. TLGs constitute
a logic family known for its high-speed and low power consumption, yet rely on
conventional transistor technology. Introducing memristors enables a more
affordable reconfiguration capability of TLGs. Through this work, we are
introducing a physical implementation of a memristor-based current-mode TLG
(MCMTLG) circuit and validate its design and operation through multiple
experimental setups. We demonstrate 2-input and 3-input MCMTLG configurations
and showcase their reconfiguration capability. This is achieved by varying
memristive weights arbitrarily for shaping the classification decision
boundary, thus showing promise as an alternative hardware-friendly
implementation of Artificial Neural Networks (ANNs). Through the employment of
real memristor devices as the equivalent of synaptic weights in TLGs, we are
realizing components that can be used towards an in-silico classifier.
