Dynamical memristors for higher-complexity neuromorphic computing
How novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems are discussed, which enable new computing architectures that offer dramatically greater computing efficiency than conventional computers.
Abstract
Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive 'complex computing'. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity. Memristors are devices that possess materials-level complex dynamics that can be used for computing, such that each memristor can functionally replace elaborate digital circuits. This Review surveys novel material properties that enable complex dynamics and new computing architectures that offer dramatically greater computing efficiency than conventional computers.