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Neuromorphic architectures are hardware systems that aim to use the principles of neural function for their basis of operation. Their goal is to harness biologically inspired concepts such as weighted connections, activation thresholds, short-and long-term potentiation, and inhibition to solve problems in distributed computation. Compared with today's methods of emulating neural function in software on conventional von Neumann hardware, neuromorphic systems provide the promise of inherently low power and fault-tolerant operation directly implemented into hardware, for application in distributed and embedded computing tasks, where the vast scaling of today's architectures does not provide a long-term solution. This mini review is intended for a general engineering audience not currently familiar with this exciting research area. It provides descriptions of some of the recent advances, including supercomputer and single-device implementations, approaches based on spiking and nonspiking neurons, machine learning hardware accelerators, and those utilizing memristive devices. Hardware implementations utilizing both conventional electronic materials and organic electronic materials are reviewed.
Nawrocki et al. (Wed,) studied this question.