  The development of memristive device technologies has reached a level of
maturity to enable the design of complex and large-scale hybrid memristive-CMOS
neural processing systems. These systems offer promising solutions for
implementing novel in-memory computing architectures for machine learning and
data analysis problems. We argue that they are also ideal building blocks for
the integration in neuromorphic electronic circuits suitable for ultra-low
power brain-inspired sensory processing systems, therefore leading to the
innovative solutions for always-on edge-computing and Internet-of-Things (IoT)
applications. Here we present a recipe for creating such systems based on
design strategies and computing principles inspired by those used in mammalian
brains. We enumerate the specifications and properties of memristive devices
required to support always-on learning in neuromorphic computing systems and to
minimize their power consumption. Finally, we discuss in what cases such
neuromorphic systems can complement conventional processing ones and highlight
the importance of exploiting the physics of both the memristive devices and of
the CMOS circuits interfaced to them.
