  Coupled oscillator-based networks are an attractive approach for implementing
hardware neural networks based on emerging nanotechnologies. However, the
readout of the state of a coupled oscillator network is a difficult challenge
in hardware implementations, as it necessitates complex signal processing to
evaluate the degree of synchronization between oscillators, possibly more
complicated than the coupled oscillator network itself. In this work, we focus
on a coupled oscillator network particularly adapted to emerging technologies,
and evaluate two schemes for reading synchronization patterns that can be
readily implemented with basic CMOS circuits. Through simulation of a simple
generic coupled oscillator network, we compare the operation of these readout
techniques with a previously proposed full statistics evaluation scheme. Our
approaches provide results nearly identical to the mathematical method, but
also show better resilience to moderate noise, which is a major concern for
hardware implementations. These results open the door to widespread realization
of hardware coupled oscillator-based neural systems.
