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

A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems