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In this work we have extended AutoML inspired approaches to the explorationand optimization of neuromorphic architectures. Through the integration of aparallel asynchronous model-based search approach with a simulation frameworkto simulate spiking architectures, we are able to efficiently explore theconfiguration space of neuromorphic architectures and identify the subset ofconditions leading to the highest performance in a targeted application. Wehave demonstrated this approach on an exemplar case of real time, on-chiplearning application. Our results indicate that we can effectively useoptimization approaches to optimize complex architectures, therefore providinga viable pathway towards application-driven codesign.

AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures