  In this work we have extended AutoML inspired approaches to the exploration
and optimization of neuromorphic architectures. Through the integration of a
parallel asynchronous model-based search approach with a simulation framework
to simulate spiking architectures, we are able to efficiently explore the
configuration space of neuromorphic architectures and identify the subset of
conditions leading to the highest performance in a targeted application. We
have demonstrated this approach on an exemplar case of real time, on-chip
learning application. Our results indicate that we can effectively use
optimization approaches to optimize complex architectures, therefore providing
a viable pathway towards application-driven codesign.
