  As the compute demands for machine learning and artificial intelligence
applications continue to grow, neuromorphic hardware has been touted as a
potential solution. New emerging devices like memristors, atomic switches, etc
have shown tremendous potential to replace CMOS-based circuits but have been
hindered by multiple challenges with respect to device variability, stochastic
behavior and scalability. In this paper we will introduce a
Description<->Design framework to analyze past successes in computing,
understand current problems and identify solutions moving forward. Engineering
systems with these emerging devices might require the modification of both the
type of descriptions of learning that we will design for, and the design
methodologies we employ in order to realize these new descriptions. We will
explore ideas from complexity engineering and analyze the advantages and
challenges they offer over traditional approaches to neuromorphic design with
novel computing fabrics. A reservoir computing example is used to understand
the specific changes that would accompany in moving towards a complexity
engineering approach. The time is ideal for a significant reboot of our design
methodologies and success will represent a radical shift in how neuromorphic
hardware is designed and pave the way for a new paradigm.
