  Increasing complexity and data-generation rates in cyber-physical systems and
the industrial Internet of things are calling for a corresponding increase in
AI capabilities at the resource-constrained edges of the Internet. Meanwhile,
the resource requirements of digital computing and deep learning are growing
exponentially, in an unsustainable manner. One possible way to bridge this gap
is the adoption of resource-efficient brain-inspired "neuromorphic" processing
and sensing devices, which use event-driven, asynchronous, dynamic
neurosynaptic elements with colocated memory for distributed processing and
machine learning. However, since neuromorphic systems are fundamentally
different from conventional von Neumann computers and clock-driven sensor
systems, several challenges are posed to large-scale adoption and integration
of neuromorphic devices into the existing distributed digital-computational
infrastructure. Here, we describe the current landscape of neuromorphic
computing, focusing on characteristics that pose integration challenges. Based
on this analysis, we propose a microservice-based framework for neuromorphic
systems integration, consisting of a neuromorphic-system proxy, which provides
virtualization and communication capabilities required in distributed systems
of systems, in combination with a declarative programming approach offering
engineering-process abstraction. We also present concepts that could serve as a
basis for the realization of this framework, and identify directions for
further research required to enable large-scale system integration of
neuromorphic devices.
