Memristive and CMOS devices for neuromorphic computing

Abstract

Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of vonNeumann architecture of conventionaldigital processors. The aimof neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, somemajor challenges have to be faced. Since the brain processes information by high-density neural networkswith ultra-lowpower consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.